{
  "nbformat": 4,
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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "transformer_chatbot_tf2_fix.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "NC-jvvwAyW4D"
      },
      "source": [
        "##### Copyright 2019 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab_type": "code",
        "id": "bw8YpxF4yT8a",
        "colab": {}
      },
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "nd8ddC7NQ8uZ"
      },
      "source": [
        "# Transformer Chatbot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "YcK7-_3RxRvK"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/examples/blob/master/community/en/transformer_chatbot.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/tensorflow/examples/blob/master/community/en/transformer_chatbot.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rHMPkA2eQrpT"
      },
      "source": [
        "This tutorial trains a <a href=\"https://arxiv.org/abs/1706.03762\" class=\"external\">Transformer model</a> to be a chatbot. This is an advanced example that assumes knowledge of [text generation](https://tensorflow.org/alpha/tutorials/text/text_generation), [attention](https://www.tensorflow.org/alpha/tutorials/text/nmt_with_attention) and [transformer](https://www.tensorflow.org/alpha/tutorials/text/transformer).\n",
        "\n",
        "The core idea behind the Transformer model is *self-attention*—the ability to attend to different positions of the input sequence to compute a representation of that sequence. Transformer creates stacks of self-attention layers and is explained below in the sections *Scaled dot product attention* and *Multi-head attention*.\n",
        "\n",
        "Note: The model architecture is identical to the example in [Transformer model for language understanding](https://www.tensorflow.org/alpha/tutorials/text/transformer), and we demonstrate how to implement the same model in the Functional approach instead of Subclassing."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mb_5bl7G_n30",
        "outputId": "c3ebeb6e-2ee3-4128-a369-a22adb0f4178",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        }
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "try:\n",
        "  # The %tensorflow_version magic only works in colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n",
        "import tensorflow as tf\n",
        "tf.random.set_seed(1234)\n",
        "\n",
        "!pip install tensorflow-datasets==1.2.0\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "import os\n",
        "import re\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n",
            "Collecting tensorflow-datasets==1.2.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/6c/34/ff424223ed4331006aaa929efc8360b6459d427063dc59fc7b75d7e4bab3/tensorflow_datasets-1.2.0-py3-none-any.whl (2.3MB)\n",
            "\u001b[K     |████████████████████████████████| 2.3MB 2.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: absl-py in /tensorflow-2.0.0-rc2/python3.6 (from tensorflow-datasets==1.2.0) (0.8.0)\n",
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            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets==1.2.0) (0.16.0)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets==1.2.0) (5.4.8)\n",
            "Requirement already satisfied: wrapt in /tensorflow-2.0.0-rc2/python3.6 (from tensorflow-datasets==1.2.0) (1.11.2)\n",
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            "Requirement already satisfied: numpy in /tensorflow-2.0.0-rc2/python3.6 (from tensorflow-datasets==1.2.0) (1.17.2)\n",
            "Requirement already satisfied: dill in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets==1.2.0) (0.3.0)\n",
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            "Requirement already satisfied: googleapis-common-protos in /usr/local/lib/python3.6/dist-packages (from tensorflow-metadata->tensorflow-datasets==1.2.0) (1.6.0)\n",
            "Requirement already satisfied: setuptools in /tensorflow-2.0.0-rc2/python3.6 (from protobuf>=3.6.1->tensorflow-datasets==1.2.0) (41.2.0)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->tensorflow-datasets==1.2.0) (3.0.4)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->tensorflow-datasets==1.2.0) (1.24.3)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->tensorflow-datasets==1.2.0) (2.8)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->tensorflow-datasets==1.2.0) (2019.9.11)\n",
            "Installing collected packages: tensorflow-datasets\n",
            "Successfully installed tensorflow-datasets-1.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "y0AqALdZCbCW"
      },
      "source": [
        "##Prepare Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "awb4RH3XCobf"
      },
      "source": [
        "We will use the conversations in movies and TV shows provided by [Cornell Movie-Dialogs Corpus](https://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html), which contains more than 220 thousands conversational exchanges between more than 10k pairs of movie characters, as our dataset.\n",
        "\n",
        "`movie_conversations.txt` contains list of the conversation IDs and `movie_lines.text` contains the text of assoicated with each conversation ID. For further  information regarding the dataset, please check the README file in the zip file.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "S17Nfn6W_vhd",
        "outputId": "43cd9b24-8a76-4b79-df2b-b80035a2ad22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "path_to_zip = tf.keras.utils.get_file(\n",
        "    'cornell_movie_dialogs.zip',\n",
        "    origin=\n",
        "    'http://www.cs.cornell.edu/~cristian/data/cornell_movie_dialogs_corpus.zip',\n",
        "    extract=True)\n",
        "\n",
        "path_to_dataset = os.path.join(\n",
        "    os.path.dirname(path_to_zip), \"cornell movie-dialogs corpus\")\n",
        "\n",
        "path_to_movie_lines = os.path.join(path_to_dataset, 'movie_lines.txt')\n",
        "path_to_movie_conversations = os.path.join(path_to_dataset,\n",
        "                                           'movie_conversations.txt')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from http://www.cs.cornell.edu/~cristian/data/cornell_movie_dialogs_corpus.zip\n",
            "9920512/9916637 [==============================] - 7s 1us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "iZMuzj0cVr3E"
      },
      "source": [
        "### Load and preprocess data\n",
        "\n",
        "To keep this example simple and fast, we are limiting the maximum number of training samples to`MAX_SAMPLES=25000` and the maximum length of the sentence to be `MAX_LENGTH=40`.\n",
        "\n",
        "We preprocess our dataset in the following order:\n",
        "* Extract `MAX_SAMPLES` conversation pairs into list of `questions` and `answers.\n",
        "* Preprocess each sentence by removing special characters in each sentence.\n",
        "* Build tokenizer (map text to ID and ID to text) using [TensorFlow Datasets SubwordTextEncoder](https://www.tensorflow.org/datasets/api_docs/python/tfds/features/text/SubwordTextEncoder).\n",
        "* Tokenize each sentence and add `START_TOKEN` and `END_TOKEN` to indicate the start and end of each sentence.\n",
        "* Filter out sentence that has more than `MAX_LENGTH` tokens.\n",
        "* Pad tokenized sentences to `MAX_LENGTH`\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_B147qKb_0ks",
        "colab": {}
      },
      "source": [
        "# Maximum number of samples to preprocess\n",
        "MAX_SAMPLES = 50000\n",
        "\n",
        "def preprocess_sentence(sentence):\n",
        "  sentence = sentence.lower().strip()\n",
        "  # creating a space between a word and the punctuation following it\n",
        "  # eg: \"he is a boy.\" => \"he is a boy .\"\n",
        "  sentence = re.sub(r\"([?.!,])\", r\" \\1 \", sentence)\n",
        "  sentence = re.sub(r'[\" \"]+', \" \", sentence)\n",
        "  # replacing everything with space except (a-z, A-Z, \".\", \"?\", \"!\", \",\")\n",
        "  sentence = re.sub(r\"[^a-zA-Z?.!,]+\", \" \", sentence)\n",
        "  sentence = sentence.strip()\n",
        "  # adding a start and an end token to the sentence\n",
        "  return sentence\n",
        "\n",
        "\n",
        "def load_conversations():\n",
        "  # dictionary of line id to text\n",
        "  id2line = {}\n",
        "  with open(path_to_movie_lines, errors='ignore') as file:\n",
        "    lines = file.readlines()\n",
        "  for line in lines:\n",
        "    parts = line.replace('\\n', '').split(' +++$+++ ')\n",
        "    id2line[parts[0]] = parts[4]\n",
        "\n",
        "  inputs, outputs = [], []\n",
        "  with open(path_to_movie_conversations, 'r') as file:\n",
        "    lines = file.readlines()\n",
        "  for line in lines:\n",
        "    parts = line.replace('\\n', '').split(' +++$+++ ')\n",
        "    # get conversation in a list of line ID\n",
        "    conversation = [line[1:-1] for line in parts[3][1:-1].split(', ')]\n",
        "    for i in range(len(conversation) - 1):\n",
        "      inputs.append(preprocess_sentence(id2line[conversation[i]]))\n",
        "      outputs.append(preprocess_sentence(id2line[conversation[i + 1]]))\n",
        "      if len(inputs) >= MAX_SAMPLES:\n",
        "        return inputs, outputs\n",
        "  return inputs, outputs\n",
        "\n",
        "\n",
        "questions, answers = load_conversations()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mfOOK5f7Wm6c",
        "outputId": "24993723-9696-4d25-b4ff-bc05cfb8edb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print('Sample question: {}'.format(questions[20]))\n",
        "print('Sample answer: {}'.format(answers[20]))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sample question: i really , really , really wanna go , but i can t . not unless my sister goes .\n",
            "Sample answer: i m workin on it . but she doesn t seem to be goin for him .\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "s6XX2udMTCQt",
        "colab": {}
      },
      "source": [
        "# Build tokenizer using tfds for both questions and answers\n",
        "tokenizer = tfds.features.text.SubwordTextEncoder.build_from_corpus(\n",
        "    questions + answers, target_vocab_size=2**13)\n",
        "\n",
        "# Define start and end token to indicate the start and end of a sentence\n",
        "START_TOKEN, END_TOKEN = [tokenizer.vocab_size], [tokenizer.vocab_size + 1]\n",
        "\n",
        "# Vocabulary size plus start and end token\n",
        "VOCAB_SIZE = tokenizer.vocab_size + 2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "h5h8pvRUTFt5",
        "outputId": "a54ba714-e198-4d11-d604-8ca1685f02f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print('Tokenized sample question: {}'.format(tokenizer.encode(questions[20])))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tokenized sample question: [4, 281, 3, 281, 3, 143, 395, 176, 3, 42, 4, 38, 8191, 2, 37, 873, 27, 2031, 3096, 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "YESTPgeg_XgT",
        "colab": {}
      },
      "source": [
        "# Maximum sentence length\n",
        "MAX_LENGTH = 40\n",
        "\n",
        "\n",
        "# Tokenize, filter and pad sentences\n",
        "def tokenize_and_filter(inputs, outputs):\n",
        "  tokenized_inputs, tokenized_outputs = [], []\n",
        "  \n",
        "  for (sentence1, sentence2) in zip(inputs, outputs):\n",
        "    # tokenize sentence\n",
        "    sentence1 = START_TOKEN + tokenizer.encode(sentence1) + END_TOKEN\n",
        "    sentence2 = START_TOKEN + tokenizer.encode(sentence2) + END_TOKEN\n",
        "    # check tokenized sentence max length\n",
        "    if len(sentence1) <= MAX_LENGTH and len(sentence2) <= MAX_LENGTH:\n",
        "      tokenized_inputs.append(sentence1)\n",
        "      tokenized_outputs.append(sentence2)\n",
        "  \n",
        "  # pad tokenized sentences\n",
        "  tokenized_inputs = tf.keras.preprocessing.sequence.pad_sequences(\n",
        "      tokenized_inputs, maxlen=MAX_LENGTH, padding='post')\n",
        "  tokenized_outputs = tf.keras.preprocessing.sequence.pad_sequences(\n",
        "      tokenized_outputs, maxlen=MAX_LENGTH, padding='post')\n",
        "  \n",
        "  return tokenized_inputs, tokenized_outputs\n",
        "\n",
        "\n",
        "questions, answers = tokenize_and_filter(questions, answers)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "pohHm8IRWlIH",
        "outputId": "10448ea8-2d4b-439b-ed9d-2a13ed7bf721",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print('Vocab size: {}'.format(VOCAB_SIZE))\n",
        "print('Number of samples: {}'.format(len(questions)))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Vocab size: 8333\n",
            "Number of samples: 44095\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "S50jT4upWh5c"
      },
      "source": [
        "### Create `tf.data.Dataset`\n",
        "\n",
        "We are going to use the [tf.data.Dataset API](https://www.tensorflow.org/api_docs/python/tf/data) to contruct our input pipline in order to utilize features like caching and prefetching to speed up the training process.\n",
        "\n",
        "The transformer is an auto-regressive model: it makes predictions one part at a time, and uses its output so far to decide what to do next.\n",
        "\n",
        "During training this example uses teacher-forcing. Teacher forcing is passing the true output to the next time step regardless of what the model predicts at the current time step.\n",
        "\n",
        "As the transformer predicts each word, self-attention allows it to look at the previous words in the input sequence to better predict the next word.\n",
        "\n",
        "To prevent the model from peaking at the expected output the model uses a look-ahead mask.\n",
        "\n",
        "Target is divided into `decoder_inputs` which padded as an input to the decoder and `cropped_targets` for calculating our loss and accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "pttC3XxgAXWQ",
        "outputId": "225aeed3-0b29-4e49-c0bb-e416b104de7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "source": [
        "BATCH_SIZE = 64\n",
        "BUFFER_SIZE = 20000\n",
        "\n",
        "# decoder inputs use the previous target as input\n",
        "# remove START_TOKEN from targets\n",
        "dataset = tf.data.Dataset.from_tensor_slices((\n",
        "    {\n",
        "        'inputs': questions,\n",
        "        'dec_inputs': answers[:, :-1]\n",
        "    },\n",
        "    {\n",
        "        'outputs': answers[:, 1:]\n",
        "    },\n",
        "))\n",
        "\n",
        "dataset = dataset.cache()\n",
        "dataset = dataset.shuffle(BUFFER_SIZE)\n",
        "dataset = dataset.batch(BATCH_SIZE)\n",
        "dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /tensorflow-2.0.0-rc2/python3.6/tensorflow_core/python/data/util/random_seed.py:58: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /tensorflow-2.0.0-rc2/python3.6/tensorflow_core/python/data/util/random_seed.py:58: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mU8yNWpwPlS7",
        "outputId": "de2c0206-984a-4770-8c23-399e44acbfe9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "print(dataset)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<PrefetchDataset shapes: ({inputs: (None, 40), dec_inputs: (None, 39)}, {outputs: (None, 39)}), types: ({inputs: tf.int32, dec_inputs: tf.int32}, {outputs: tf.int32})>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "s9eeMPjGXmI1"
      },
      "source": [
        "## Attention\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "uctkwvPZVSzu"
      },
      "source": [
        "### Scaled dot product Attention\n",
        "\n",
        "The scaled dot-product attention function used by the transformer takes three inputs: Q (query), K (key), V (value). The equation used to calculate the attention weights is:\n",
        "\n",
        "$$\\Large{Attention(Q, K, V) = softmax_k(\\frac{QK^T}{\\sqrt{d_k}}) V} $$\n",
        "\n",
        "As the softmax normalization is done on the `key`, its values decide the amount of importance given to the `query`.\n",
        "\n",
        "The output represents the multiplication of the attention weights and the `value` vector. This ensures that the words we want to focus on are kept as is and the irrelevant words are flushed out.\n",
        "\n",
        "The dot-product attention is scaled by a factor of square root of the depth. This is done because for large values of depth, the dot product grows large in magnitude pushing the softmax function where it has small gradients resulting in a very hard softmax. \n",
        "\n",
        "For example, consider that `query` and `key` have a mean of 0 and variance of 1. Their matrix multiplication will have a mean of 0 and variance of `dk`. Hence, *square root of `dk`* is used for scaling (and not any other number) because the matmul of `query` and `key` should have a mean of 0 and variance of 1, so that we get a gentler softmax.\n",
        "\n",
        "The mask is multiplied with *-1e9 (close to negative infinity).* This is done because the mask is summed with the scaled matrix multiplication of `query` and `key` and is applied immediately before a softmax. The goal is to zero out these cells, and large negative inputs to softmax are near zero in the output."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ENfqAFna_50H",
        "colab": {}
      },
      "source": [
        "def scaled_dot_product_attention(query, key, value, mask):\n",
        "  \"\"\"Calculate the attention weights. \"\"\"\n",
        "  matmul_qk = tf.matmul(query, key, transpose_b=True)\n",
        "\n",
        "  # scale matmul_qk\n",
        "  depth = tf.cast(tf.shape(key)[-1], tf.float32)\n",
        "  logits = matmul_qk / tf.math.sqrt(depth)\n",
        "\n",
        "  # add the mask to zero out padding tokens\n",
        "  if mask is not None:\n",
        "    logits += (mask * -1e9)\n",
        "\n",
        "  # softmax is normalized on the last axis (seq_len_k)\n",
        "  attention_weights = tf.nn.softmax(logits, axis=-1)\n",
        "\n",
        "  output = tf.matmul(attention_weights, value)\n",
        "\n",
        "  return output"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "XwmOB9HvVbyh"
      },
      "source": [
        "### Multi-head attention\n",
        "\n",
        "<img src=\"https://www.tensorflow.org/images/tutorials/transformer/multi_head_attention.png\" width=\"500\" alt=\"multi-head attention\">\n",
        "\n",
        "\n",
        "Multi-head attention consists of four parts:\n",
        "* Linear layers and split into heads.\n",
        "* Scaled dot-product attention.\n",
        "* Concatenation of heads.\n",
        "* Final linear layer.\n",
        "\n",
        "Each multi-head attention block gets three inputs; Q (query), K (key), V (value). These are put through linear (Dense) layers and split up into multiple heads. \n",
        "\n",
        "The `scaled_dot_product_attention` defined above is applied to each head (broadcasted for efficiency). An appropriate mask must be used in the attention step.  The attention output for each head is then concatenated (using `tf.transpose`, and `tf.reshape`) and put through a final `Dense` layer.\n",
        "\n",
        "Instead of one single attention head, `query`, `key`, and `value` are split into multiple heads because it allows the model to jointly attend to information at different positions from different representational spaces. After the split each head has a reduced dimensionality, so the total computation cost is the same as a single head attention with full dimensionality."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "L9eYssGIAG4h",
        "colab": {}
      },
      "source": [
        "class MultiHeadAttention(tf.keras.layers.Layer):\n",
        "\n",
        "  def __init__(self, d_model, num_heads, name=\"multi_head_attention\"):\n",
        "    super(MultiHeadAttention, self).__init__(name=name)\n",
        "    self.num_heads = num_heads\n",
        "    self.d_model = d_model\n",
        "\n",
        "    assert d_model % self.num_heads == 0\n",
        "\n",
        "    self.depth = d_model // self.num_heads\n",
        "\n",
        "    self.query_dense = tf.keras.layers.Dense(units=d_model)\n",
        "    self.key_dense = tf.keras.layers.Dense(units=d_model)\n",
        "    self.value_dense = tf.keras.layers.Dense(units=d_model)\n",
        "\n",
        "    self.dense = tf.keras.layers.Dense(units=d_model)\n",
        "\n",
        "  def split_heads(self, inputs, batch_size):\n",
        "    inputs = tf.reshape(\n",
        "        inputs, shape=(batch_size, -1, self.num_heads, self.depth))\n",
        "    return tf.transpose(inputs, perm=[0, 2, 1, 3])\n",
        "\n",
        "  def call(self, inputs):\n",
        "    query, key, value, mask = inputs['query'], inputs['key'], inputs[\n",
        "        'value'], inputs['mask']\n",
        "    batch_size = tf.shape(query)[0]\n",
        "\n",
        "    # linear layers\n",
        "    query = self.query_dense(query)\n",
        "    key = self.key_dense(key)\n",
        "    value = self.value_dense(value)\n",
        "\n",
        "    # split heads\n",
        "    query = self.split_heads(query, batch_size)\n",
        "    key = self.split_heads(key, batch_size)\n",
        "    value = self.split_heads(value, batch_size)\n",
        "\n",
        "    # scaled dot-product attention\n",
        "    scaled_attention = scaled_dot_product_attention(query, key, value, mask)\n",
        "\n",
        "    scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])\n",
        "\n",
        "    # concatenation of heads\n",
        "    concat_attention = tf.reshape(scaled_attention,\n",
        "                                  (batch_size, -1, self.d_model))\n",
        "\n",
        "    # final linear layer\n",
        "    outputs = self.dense(concat_attention)\n",
        "\n",
        "    return outputs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "eDUX7Oa8Xudj"
      },
      "source": [
        "## Transformer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "x5QlgXsxYirg"
      },
      "source": [
        "### Masking\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "0CX5H8A-Wybj"
      },
      "source": [
        "`create_padding_mask` and `create_look_ahead` are helper functions to creating masks to mask out padded tokens, we are going to use these helper functions as `tf.keras.layers.Lambda` layers.\n",
        "\n",
        "Mask all the pad tokens (value `0`) in the batch to ensure the model does not treat padding as input."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "imCQ0jrvWhC7",
        "colab": {}
      },
      "source": [
        "def create_padding_mask(x):\n",
        "  mask = tf.cast(tf.math.equal(x, 0), tf.float32)\n",
        "  # (batch_size, 1, 1, sequence length)\n",
        "  return mask[:, tf.newaxis, tf.newaxis, :]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "IrwtsqrfWd-3",
        "outputId": "b7ca580c-ab44-4558-b1b0-d567bc27e660",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "print(create_padding_mask(tf.constant([[1, 2, 0, 3, 0], [0, 0, 0, 4, 5]])))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[[[0. 0. 1. 0. 1.]]]\n",
            "\n",
            "\n",
            " [[[1. 1. 1. 0. 0.]]]], shape=(2, 1, 1, 5), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "qJAicy1zW1QT"
      },
      "source": [
        "Look-ahead mask to mask the future tokens in a sequence.\n",
        "We also mask out pad tokens.\n",
        "\n",
        "i.e. To predict the third word, only the first and second word will be used"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "HSVdD2zKWaXx",
        "colab": {}
      },
      "source": [
        "def create_look_ahead_mask(x):\n",
        "  seq_len = tf.shape(x)[1]\n",
        "  look_ahead_mask = 1 - tf.linalg.band_part(tf.ones((seq_len, seq_len)), -1, 0)\n",
        "  padding_mask = create_padding_mask(x)\n",
        "  return tf.maximum(look_ahead_mask, padding_mask)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "xhwz9xzxWcod",
        "outputId": "23729998-d7e2-4780-a1e4-ee7cf361af90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "print(create_look_ahead_mask(tf.constant([[1, 2, 0, 4, 5]])))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[[[0. 1. 1. 1. 1.]\n",
            "   [0. 0. 1. 1. 1.]\n",
            "   [0. 0. 1. 1. 1.]\n",
            "   [0. 0. 1. 0. 1.]\n",
            "   [0. 0. 1. 0. 0.]]]], shape=(1, 1, 5, 5), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "TpR7kz4jFkPJ"
      },
      "source": [
        "### Positional encoding\n",
        "\n",
        "Since this model doesn't contain any recurrence or convolution, positional encoding is added to give the model some information about the relative position of the words in the sentence. \n",
        "\n",
        "The positional encoding vector is added to the embedding vector. Embeddings represent a token in a d-dimensional space where tokens with similar meaning will be closer to each other. But the embeddings do not encode the relative position of words in a sentence. So after adding the positional encoding, words will be closer to each other based on the *similarity of their meaning and their position in the sentence*, in the d-dimensional space.\n",
        "\n",
        "See the notebook on [positional encoding](https://github.com/tensorflow/examples/blob/master/community/en/position_encoding.ipynb) to learn more about it. The formula for calculating the positional encoding is as follows:\n",
        "\n",
        "$$\\Large{PE_{(pos, 2i)} = sin(pos / 10000^{2i / d_{model}})} $$\n",
        "$$\\Large{PE_{(pos, 2i+1)} = cos(pos / 10000^{2i / d_{model}})} $$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-9Oibz2es-qW",
        "colab": {}
      },
      "source": [
        "class PositionalEncoding(tf.keras.layers.Layer):\n",
        "\n",
        "  def __init__(self, position, d_model):\n",
        "    super(PositionalEncoding, self).__init__()\n",
        "    self.pos_encoding = self.positional_encoding(position, d_model)\n",
        "\n",
        "  def get_angles(self, position, i, d_model):\n",
        "    angles = 1 / tf.pow(10000, (2 * (i // 2)) / tf.cast(d_model, tf.float32))\n",
        "    return position * angles\n",
        "\n",
        "  def positional_encoding(self, position, d_model):\n",
        "    angle_rads = self.get_angles(\n",
        "        position=tf.range(position, dtype=tf.float32)[:, tf.newaxis],\n",
        "        i=tf.range(d_model, dtype=tf.float32)[tf.newaxis, :],\n",
        "        d_model=d_model)\n",
        "    # apply sin to even index in the array\n",
        "    sines = tf.math.sin(angle_rads[:, 0::2])\n",
        "    # apply cos to odd index in the array\n",
        "    cosines = tf.math.cos(angle_rads[:, 1::2])\n",
        "\n",
        "    pos_encoding = tf.concat([sines, cosines], axis=-1)\n",
        "    pos_encoding = pos_encoding[tf.newaxis, ...]\n",
        "    return tf.cast(pos_encoding, tf.float32)\n",
        "\n",
        "  def call(self, inputs):\n",
        "    return inputs + self.pos_encoding[:, :tf.shape(inputs)[1], :]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "UC_fQehi3_Yh",
        "outputId": "b8af00e4-c47d-444d-fd44-5a8cb0f94d7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "sample_pos_encoding = PositionalEncoding(50, 512)\n",
        "\n",
        "plt.pcolormesh(sample_pos_encoding.pos_encoding.numpy()[0], cmap='RdBu')\n",
        "plt.xlabel('Depth')\n",
        "plt.xlim((0, 512))\n",
        "plt.ylabel('Position')\n",
        "plt.colorbar()\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ubXWHwJFortZRSEXriNt3N1sDSzSVjNlafN4Sj99vz0Wwt83W+qLYRMlBR/HM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\npZQaQP4XrNHoBZRdWLQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "HVazCemoW2Ye"
      },
      "source": [
        "### Encoder Layer\n",
        "\n",
        "Each encoder layer consists of sublayers:\n",
        "\n",
        "1. Multi-head attention (with padding mask) \n",
        "2. 2 dense layers followed by dropout\n",
        "\n",
        "Each of these sublayers has a residual connection around it followed by a layer normalization. Residual connections help in avoiding the vanishing gradient problem in deep networks.\n",
        "\n",
        "The output of each sublayer is `LayerNorm(x + Sublayer(x))`. The normalization is done on the `d_model` (last) axis."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5guJOLJmfcuX",
        "colab": {}
      },
      "source": [
        "def encoder_layer(units, d_model, num_heads, dropout, name=\"encoder_layer\"):\n",
        "  inputs = tf.keras.Input(shape=(None, d_model), name=\"inputs\")\n",
        "  padding_mask = tf.keras.Input(shape=(1, 1, None), name=\"padding_mask\")\n",
        "\n",
        "  attention = MultiHeadAttention(\n",
        "      d_model, num_heads, name=\"attention\")({\n",
        "          'query': inputs,\n",
        "          'key': inputs,\n",
        "          'value': inputs,\n",
        "          'mask': padding_mask\n",
        "      })\n",
        "  attention = tf.keras.layers.Dropout(rate=dropout)(attention)\n",
        "  attention = tf.keras.layers.LayerNormalization(\n",
        "      epsilon=1e-6)(inputs + attention)\n",
        "\n",
        "  outputs = tf.keras.layers.Dense(units=units, activation='relu')(attention)\n",
        "  outputs = tf.keras.layers.Dense(units=d_model)(outputs)\n",
        "  outputs = tf.keras.layers.Dropout(rate=dropout)(outputs)\n",
        "  outputs = tf.keras.layers.LayerNormalization(\n",
        "      epsilon=1e-6)(attention + outputs)\n",
        "\n",
        "  return tf.keras.Model(\n",
        "      inputs=[inputs, padding_mask], outputs=outputs, name=name)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "K16BIGSKfkve",
        "outputId": "1bc30cb1-5c52-4c53-dec0-3dd5f0d525c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "sample_encoder_layer = encoder_layer(\n",
        "    units=512,\n",
        "    d_model=128,\n",
        "    num_heads=4,\n",
        "    dropout=0.3,\n",
        "    name=\"sample_encoder_layer\")\n",
        "\n",
        "tf.keras.utils.plot_model(\n",
        "    sample_encoder_layer, to_file='encoder_layer.png', show_shapes=True)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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PHjxYtG/fXlRWVmqWVU1YnJiYqLW/vnNRXROy8s4RIiKiVmrTpk3IzMxs8cdo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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9r8lWGClfi_1"
      },
      "source": [
        "### Encoder\n",
        "\n",
        "The Encoder consists of:\n",
        "1.   Input Embedding\n",
        "2.   Positional Encoding\n",
        "3.   `num_layers` encoder layers\n",
        "\n",
        "The input is put through an embedding which is summed with the positional encoding. The output of this summation is the input to the encoder layers. The output of the encoder is the input to the decoder."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LRfugon5Wy-Y",
        "colab": {}
      },
      "source": [
        "def encoder(vocab_size,\n",
        "            num_layers,\n",
        "            units,\n",
        "            d_model,\n",
        "            num_heads,\n",
        "            dropout,\n",
        "            name=\"encoder\"):\n",
        "  inputs = tf.keras.Input(shape=(None,), name=\"inputs\")\n",
        "  padding_mask = tf.keras.Input(shape=(1, 1, None), name=\"padding_mask\")\n",
        "\n",
        "  embeddings = tf.keras.layers.Embedding(vocab_size, d_model)(inputs)\n",
        "  embeddings *= tf.math.sqrt(tf.cast(d_model, tf.float32))\n",
        "  embeddings = PositionalEncoding(vocab_size, d_model)(embeddings)\n",
        "\n",
        "  outputs = tf.keras.layers.Dropout(rate=dropout)(embeddings)\n",
        "\n",
        "  for i in range(num_layers):\n",
        "    outputs = encoder_layer(\n",
        "        units=units,\n",
        "        d_model=d_model,\n",
        "        num_heads=num_heads,\n",
        "        dropout=dropout,\n",
        "        name=\"encoder_layer_{}\".format(i),\n",
        "    )([outputs, padding_mask])\n",
        "\n",
        "  return tf.keras.Model(\n",
        "      inputs=[inputs, padding_mask], outputs=outputs, name=name)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "bNxCnjrvglnx",
        "outputId": "6fe35d8a-2ed9-4e25-e5d6-5342b293a066",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        }
      },
      "source": [
        "sample_encoder = encoder(\n",
        "    vocab_size=8192,\n",
        "    num_layers=2,\n",
        "    units=512,\n",
        "    d_model=128,\n",
        "    num_heads=4,\n",
        "    dropout=0.3,\n",
        "    name=\"sample_encoder\")\n",
        "\n",
        "tf.keras.utils.plot_model(\n",
        "   sample_encoder, to_file='encoder.png', show_shapes=True)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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mzJnaunWrvL299fDDD9sdv+GGG2Q2m3X48OE2yRkAAACdAwvNoFVqa2t1/vx5VVdX6+jR\no0pJSVFoaKgSExNtfSIiIlRSUqLs7GxZrVadPXtWJ0+erBfL399fRUVFKigoUHl5uU6ePKm5c+fq\ns88+U1VVlT799FOdPHnSVojt2bOnxVtSdKTzbKiQtFqtOnPmjHJzc21FYWhoqCTp3Xff1eXLl3Xi\nxAm77TG+b968efruu+/09ttva9KkSXbHzGaz5syZo9dff10ZGRkqKytTTU2NCgsL9fXXXzf3EgEA\nAKCToih0YRs3btTw4cMlSYsXL1Z8fLwyMjJs7+wMHjxYX3zxhTZv3qxFixZJksaPH68TJ07YYly+\nfFlRUVHy9PRUbGysBgwYoPfff18eHh62PvPnz9eYMWM0a9YsDRw4UE8//bTtncDo6Gjbtg7z5s1T\nQECAIiMjNXHiRHXv3l01NTWKiYmRxWLR3Xffrblz5+qxxx5r8jkePHhQt9xyi/785z9LkgYNGqTU\n1NQOdZ5btmxRRESE8vPzVVpaattj0GQy2fY+fPPNN2WxWCRJUVFRWrx4sTZt2qSgoCA9+eSTuv32\n2yVd2WakbhxJ+slPfqKhQ4dqzpw5cnOr/2BAWlqaFixYoGeffVbXXXedgoKClJKSovPnz+u5557T\nunXrJEkDBgzQK6+80uTrDgAAgM7DZLDpWJdiMpm0bdu2dnkfae7cucrKytK3337b5mM5U2c/z7i4\nOG3cuFFhYWHtPnZ7/n3sKqZPny6Jd+euhesDAIDDZTFTiFap2wKhq+tM5/n9x1GPHj0qs9nslIIQ\nAAAAnQNFIdDFLF68WCdOnNC//vUvzZkzR08//bSzU0Ibmzt3rt1jx7Nnz67X591339WSJUtUW1ur\nKVOmKDQ0VGazWcHBwYqPj9fRo0ebNWZjcd588009++yz9f6HSnZ2tl2uvXv3bvmJAwAAh6AoRIss\nXbpUmZmZKi0tVVhYmHbs2OHslNpEZzxPi8WiH/3oR7rzzju1YsUKRUZGOjsltAN/f3/t2bNHn3/+\nubZs2WJ3bPny5UpPT9fSpUtVW1urDz74QK+99ppKSkp04MABVVZWatSoUSoqKmryeI3FmTx5ssxm\ns8aOHasLFy7YvhcfH6/CwkLt379fEydOdNj5AwCAlqMoRIukpqbqu+++k2EY+vLLLzVt2jRnp9Qm\nOuN5rl69WjU1NTp16lS9FUddTWVlpWJiYjr9GE3h6emp8ePHa8CAAXYLIK1du1Zbt27V9u3b5e3t\nLenKwkcjR46UxWJRWFiY1qxZo9LSUr388svNGrOxOMnJybr11ls1ceJEVVdXS7rynmlwcLBiY2N1\n0003OeTcAQBA61AUAuiytmzZouLi4k4/Rkvl5eVp2bJlWrlypcxms6Qr+2m+9dZbdv3q9rvMz89v\ncuymxlmxYoUOHz6stLS0Fp0DAABoexSFADoMwzC0bt06DRo0SB4eHvLz89M999yjzz77zNYnKSnJ\ntlVHnUcffVReXl4ymUw6d+6cJCklJUWLFi1Sfn6+TCaTIiIilJ6eLrPZrICAAM2dO1dBQUEym82K\niYmx2+uxNWNI0t69e9ttD82GpKenyzAMTZ48ucF+lZWVkiQfH59WjXe1OH5+fho9erTS0tLEYtcA\nAHRMFIUAOowVK1ZoyZIlevLJJ1VcXKz9+/fr9OnTio2N1ZkzZyRdKXR+uMXFpk2btHLlSru2tLQ0\nTZo0Sf3795dhGMrLy1NSUpISExN16dIlJScnq6CgQIcOHVJ1dbXuuusu2x6PrRlD+vdqtbW1tY67\nOC2wa9cuDRw40LbH5bV8/PHHkq7sc9ka14ozdOhQffXVVzpy5Eir4gMAgLZBUQigQ6isrNS6det0\n7733avbs2fL19VVUVJReeOEFnTt3Ti+++KLDxnJzc7PNRkZGRiojI0Pl5eXKzMx0SPy4uDiVlZVp\n2bJlDonXEhUVFfryyy/Vv3//a/Y5c+aMtm7dquTkZEVHRzc6o9jSOHXvDh47dqxF8QEAQNtyc3YC\nACBJx48f18WLFzVs2DC79uHDh6tHjx52j3c62rBhw2SxWOweU+3siouLZRhGg7OE0dHRqqio0IwZ\nM7R69Wq5u7u3aKzG4tTlUDfbCwAAOhaKQgAdQt22BT179qx3rFevXiovL2/T8T08PHT27Nk2HaM9\nXb58WZLsViL9oYCAAG3ZskU333xzq8ZqLI6np6ddTgAAoGPh8VEAHUKvXr0k6arF34ULFxQSEtJm\nY1ut1jYfo73VFWI/3Dz++/r06WO77q3RWJyqqiq7nAAAQMfCTCGADuGWW25Rz5499be//c2u/eDB\ng6qqqtJtt91ma3Nzc5PVanXY2Lm5uTIMQyNGjGizMdpbQECATCaTSktLr9nnh1tKtFRjcepyCAwM\ndMh4AADAsZgpBNAhmM1mLVq0SG+88YZeeeUVlZWV6dixY5o3b56CgoL0yCOP2PpGRESopKRE2dnZ\nslqtOnv2rE6ePFkvpr+/v4qKilRQUKDy8nJbkVdbW6vz58+rurpaR48eVUpKikJDQ5WYmOiQMfbs\n2eP0LSksFovCw8NVWFh41eN5eXkKDAzUzJkz6x1LSEhQYGCgDh061Og4DcWpU5dDVFRUE7MHAADt\niaIQQIexfPlypaamatWqVerdu7dGjx6tfv36KTc3V15eXrZ+8+fP15gxYzRr1iwNHDhQTz/9tO3R\nxOjoaNvWEvPmzVNAQIAiIyM1ceJElZSUSLrybltUVJQ8PT0VGxurAQMG6P3337d7/661Y3QEcXFx\nOn78uG3/wO9raM/AqqoqFRcXKycnp9ExmrL34CeffKLg4GANHjy40b4AAKD98fgogA7DZDLpN7/5\njX7zm9802M/f31/79u2r1/7cc8/Z/Xno0KEqKCio18/b2/uaM2iOGGPChAkqKytrMH57+PWvf62M\njAzt3LlTs2fPtjt20003XXM10B07duj222/XjTfe2OgYDcWRpG+//VbvvfeeVq9eLZPJ1LwTAAAA\n7YKZQgAup6HFVzqryspKvfPOOzpx4oRtYZeIiAitWrVKq1at0sWLF5sUp6amRtnZ2SovL1dCQkKr\n81qxYoWGDBmipKQkSVdmFouKinTgwAHl5eW1Oj4AAGg9ikIA6AJKSko0fvx4DRgwQA8++KCtfcmS\nJZo+fboSEhIaXHSmTm5urnbu3Kk9e/Y0uMdhU6xbt06HDx/W7t27bXsX5uTkKDg4WLGxsdq1a1er\n4gMAAMegKATgMpYuXarMzEyVlpYqLCxMO3bscHZKDvHCCy/IMAzb55VXXrE7vmbNGiUlJemZZ55p\nNNbYsWP16quvqm/fvq3KKScnR999951yc3Pl5+dna7/nnnvscj137lyrxgEAAK3HO4UAXEZqaqpS\nU1OdnYZTjBs3TuPGjWu38eLj4xUfH99u4wEAgJZjphAAAAAAXBhFIQAAAAC4MIpCAAAAAHBhFIUA\nAAAA4MJYaKYL+utf/+rsFAAb/j42T2FhoSRp+/btTs6kYyosLFRISIiz0wAAoEsxGYZhODsJOI7J\nZHJ2CgDQpqZNm6asrCxnpwEAQFeRxUxhF0ONj/a0fft2zZw5k793AAAAnRjvFAIAAACAC6MoBAAA\nAAAXRlEIAAAAAC6MohAAAAAAXBhFIQAAAAC4MIpCAAAAAHBhFIUAAAAA4MIoCgEAAADAhVEUAgAA\nAIALoygEAAAAABdGUQgAAAAALoyiEAAAAABcGEUhAAAAALgwikIAAAAAcGEUhQAAAADgwigKAQAA\nAMCFURQCAAAAgAujKAQAAAAAF0ZRCAAAAAAujKIQAAAAAFwYRSEAAAAAuDCKQgAAAABwYRSFAAAA\nAODCKAoBAAAAwIVRFAIAAACAC6MoBAAAAAAXRlEIAAAAAC6MohAAAAAAXBhFIQAAAAC4MIpCAAAA\nAHBhFIUAAAAA4MIoCgEAAADAhVEUAgAAAIALc3N2AgA6h8LCQv3iF79QTU2Nre38+fPy9vbW7bff\nbtd34MCB+sMf/tDOGQIAAKAlKAoBNElISIhOnjyp/Pz8esf+8pe/2P151KhR7ZUWAAAAWonHRwE0\n2QMPPCB3d/dG+yUkJLRDNgAAAHAEikIATXb//ferurq6wT4333yzIiMj2ykjAAAAtBZFIYAm69+/\nvwYPHiyTyXTV4+7u7vrFL37RzlkBAACgNSgKATTLAw88oO7du1/1WHV1taZPn97OGQEAAKA1KAoB\nNMusWbNUW1tbr71bt24aMWKE+vXr1/5JAQAAoMUoCgE0S1BQkH7605+qWzf7Xx/dunXTAw884KSs\nAAAA0FIUhQCa7ec//3m9NsMwdO+99zohGwAAALQGRSGAZps2bZrde4Xdu3fXnXfeqYCAACdmBQAA\ngJagKATQbH5+frrrrrtshaFhGJo9e7aTswIAAEBLUBQCaJHZs2fbFpxxd3fXPffc4+SMAAAA0BIU\nhQBaZPLkyfLw8JAkTZo0ST179nRyRgAAAGgJikIALeLl5WWbHeTRUQAAgM7LZBiG4ewkOjOTyeTs\nFAAATjRt2jRlZWU5Ow0AAFoqy83ZGXQFKSkpio6OdnYaQLurqanRtm3bdN9997Xo+zNnzuTnp5nW\nr18vSVqwYIGTM4H07/sBAEBnRlHoANHR0ZoxY4az0wCcYsqUKTKbzS367syZM/n5aaa6GSmuWcfA\nDCEAoCvgnUIArdLSghAAAAAdA0UhAAAAALgwikIAAAAAcGEUhQAAAADgwigKAQAAAMCFURQC6PR2\n794tX19fvfXWW85OxeW8++67WrJkiWprazVlyhSFhobKbDYrODhY8fHxOnr0aLPiNRbnzTff1LPP\nPquampq2OB0AAFwSRSGATs8wDGen4JKWL1+u9PR0LV26VLW1tfrggw/02muvqaSkRAcOHFBlZaVG\njRqloqKiJsdsLM7kyZNlNps1duxYXbhwoQ3PDgAA10FRCKDTi4uLU2lpqSZNmuTsVFRZWamYmBhn\np9Hm1q5dq61bt2r79u3y9vaWdGXP1pEjR8pisSgsLExr1qxRaWmpXn755WbFbixOcnKybr31Vk2c\nOFHV1dUOPjMAAFwPRSEAONCWLVtUXFzs7DTaVF5enpYtW6aVK1fa9ql0c3Or9/hueHi4JCk/P7/J\nsZsaZ8WKFTp8+LDS0tJadA4AAODfKAoBdGoHDhxQaGioTCaTNm7cKEnKyMiQl5eXLBaLcnJyNGHC\nBPn4+CgkJESvv/667bvp6ekym80KCAjQ3LlzFRQUJLPZrJiYGB08eNDWLykpST169FDfvn1tbY8+\n+qi8vLxkMpl07tw5SVJKSooWLVqk/Px8mUwmRURESJL27t0rHx8frVmzpj0uSZtLT0+XYRiaPHly\ng/0qKyslST4+Pq0a72px/Pz8NHr0aKWlpfH4MAAArURRCKBTGzlypD788EO7tvnz52vBggWqrKyU\nt7e3tm3bpvz8fIWHh+vhhx+W1WqVdKXYS0xM1KVLl5ScnKyCggIdOnRI1dXVuuuuu3T69GlJV4qg\nGTNm2I2xadMmrVy50q4tLS1NkyZNUv/+/WUYhvLy8iTJtihKbW1tm1yD9rZr1y4NHDhQFoulwX4f\nf/yxpCv3qDWuFWfo0KH66quvdOTIkVbFBwDA1VEUAujSYmJi5OPjoz59+ighIUEVFRU6deqUXR83\nNzcNGjRIHh4eioyMVEZGhsrLy5WZmemQHOLi4lRWVqZly5Y5JJ4zVVRU6Msvv1T//v2v2efMmTPa\nunWrkpOTFR0d3eiMYkvj3HTTTZKkY8eOtSg+AAC4ws3ZCQBAe+nRo4ck2WYKr2XYsGGyWCz67LPP\n2iOtTqW4uFiGYTQ4SxgdHa2KigrNmDFDq1evlru7e4vGaixOXQ5nzpxpUXwAAHAFRSEAXIWHh4fO\nnj3r7DQ6nMuXL0u6cn2uJSAgQFu2bNHNN9/cqrEai+Pp6WmXEwAAaBkeHwWAH7Barbpw4YJCQkKc\nnUqHU1eINbR5fJ8+fdSrV69Wj9VYnKqqKrucAABAyzBTCAA/kJubK8MwNGLECFubm5tbo4+duoKA\ngACZTCaVlpZes88Pt5Roqcbi1OUQGBjokPEAAHBVzBQCcHm1tbU6f/68qoI+0wsAACAASURBVKur\ndfToUaWkpCg0NFSJiYm2PhERESopKVF2drasVqvOnj2rkydP1ovl7++voqIiFRQUqLy8XFarVXv2\n7OkyW1JYLBaFh4ersLDwqsfz8vIUGBiomTNn1juWkJCgwMBAHTp0qNFxGopTpy6HqKioJmYPAACu\nhqIQQKe2ceNGDR8+XJK0ePFixcfHKyMjQ+vXr5ckDR48WF988YU2b96sRYsWSZLGjx+vEydO2GJc\nvnxZUVFR8vT0VGxsrAYMGKD333/f7r25+fPna8yYMZo1a5YGDhyop59+2vbYYnR0tG37innz5ikg\nIECRkZGaOHGiSkpK2uU6tKe4uDgdP37ctn/g9zW0Z2BVVZWKi4uVk5PT6BhN2Xvwk08+UXBwsAYP\nHtxoXwAAcG0mg11/W8VkMmnbtm319jAD0LiO8PMzd+5cZWVl6dtvv3VaDs0xffp0SVJWVpbTcsjL\ny9OgQYOUmZmp2bNnN/l7tbW1uv3225WYmKgHH3ywVTl8++23CgkJ0erVq23FvjN0hPsBAEArZTFT\nCMDlNbRoCuqLiIjQqlWrtGrVKl28eLFJ36mpqVF2drbKy8uVkJDQ6hxWrFihIUOGKCkpqdWxAABw\ndRSFXcTw4cPVvXt3DRkyxOGxf/nLX8rb21smk0mHDx9udr/du3fL19fXYYtPtNTOnTsVHh4uk8l0\nzU+/fv0cMhb3A13dkiVLNH36dCUkJDS46Eyd3Nxc7dy5U3v27Glwj8OmWLdunQ4fPqzdu3e3eA9E\nAADwbxSFXcQnn3yiMWPGtEnsl156SZs3b25xv47yhPLUqVP1xRdfqH///vL19ZVhGDIMQ9XV1bp0\n6ZLOnDnT6n+s1uF+dA5Lly5VZmamSktLFRYWph07djg7pU5lzZo1SkpK0jPPPNNo37Fjx+rVV19V\n3759WzVmTk6OvvvuO+Xm5srPz69VsQAAwBVsSdHFmEwmZ6dQT1xcXJNmEpyle/fu8vT0lKenpwYM\nGODQ2NyPji01NVWpqanOTqNTGzdunMaNG9du48XHxys+Pr7dxgMAwBUwU9jFtNWjVE0tbtqjCDIM\nQ1lZWXrxxRcdHjs7O9uh8bgfAAAA6OgoCttZTU2Nfve73yk0NFSenp4aPHiwtm3bJklKS0uTl5eX\nunXrpttuu02BgYFyd3eXl5eXfvzjHys2NlY33HCDzGazevXqpccff7xe/Ly8PP3oRz+Sl5eXbXn9\nAwcONDkH6co/8p9//nkNHDhQHh4e8vX11W9/+9t6YzWl34EDBxQaGiqTyaSNGzdKkjIyMuTl5SWL\nxaKcnBxNmDBBPj4+CgkJ0euvv14v19TUVA0cOFCenp7q3bu3wsLClJqaardi5d69ex2+Dxz3o+X3\nAwAAAJ2IgVaRZGzbtq3J/X/zm98YHh4exo4dO4zz588bS5cuNbp162Z88sknhmEYxvLlyw1JxsGD\nB42Kigrj3Llzxvjx4w1Jxq5du4yzZ88aFRUVRlJSkiHJOHz4sC322LFjjfDwcOPLL780rFar8Y9/\n/MP4yU9+YpjNZuNf//pXk3N48sknDZPJZPzHf/yHcf78eePSpUvGpk2bDEnGp59+aovT1H6nT582\nJBkbNmyw+64k47333jNKS0uN4uJiIzY21vDy8jKqqqps/dasWWN0797dyMnJMS5dumT8/e9/NwID\nA43bb7/d7rq+/fbbhre3t7Fq1apG70H//v0NX19fu7bk5GTj2LFj9fpyP1p2P5qquT8/MIxp06YZ\n06ZNc3Ya+D/cDwBAF7CdmcJ2dPnyZWVkZGjKlCmaOnWqevXqpaeeekru7u7KzMy06xsZGSmLxaLr\nrrtOs2bNkiSFhoaqd+/eslgstr3BPvvsM7vveXt7q1+/fnJzc9PNN9+szZs36/Lly7ZH+xrLobKy\nUuvXr9edd96phQsXqlevXvL09JS/v7/dOE3t15iYmBj5+PioT58+SkhIUEVFhU6dOmU7np2drdtu\nu02TJ0+Wp6enfvzjHys+Pl779+9XVVWVrV9cXJzKysq0bNmyJo1bWlpqt+rof/7nfzbYn/txRVPv\nBwAAADoPFpppR59//rkuXbqkW265xdbm6empvn371ismvq9Hjx6SpOrqaltb3btqVqu1wTGjoqLk\n6+uro0ePNimHvLw8Xbp0SWPHjm0wblP7NUfdeX7/nC5fviyz2WzXr6amRu7u7urevXuLx/L19dWF\nCxdsf05JSWl2ntyPK1p7P/7617+2PEkXVFhYKEnavn27kzOBdOV+hISEODsNAABahaKwHVVUVEiS\nnnrqKT311FN2x4KCgtpsXHd3d9s/7BvLoe4fnH369GkwZlP7tdbEiRP1/PPPKycnR+PGjdPx48eV\nnZ2tu+++u1VF4Q+lpaU5LFZjuB/20tLS2vX6dxUzZ850dgr4P9OmTXN2CgAAtAqPj7ajun+wr1+/\n3rZHXt2nrWZLqqurVVJSotDQ0CblUDcL9N133zUYt6n9WmvFihW64447lJiYKB8fH917772aMWNG\nk/bp64i4H/Vt27at3rnzufZn2rRpmjZtmtPz4PPv+wEAQGdHUdiO6laqPHz4cLuN+f7776u2tlY/\n/vGPm5TDLbfcom7duukvf/lLg3Gb2q+1jh8/rvz8fJ09e1ZWq1WnTp1SRkZGm21a/fXXX2vOnDlt\nElvifgAAAKDjoShsR2azWXPmzNHrr7+ujIwMlZWVqaamRoWFhfr6668dMkZVVZVKS0tVXV2tQ4cO\nKSkpSTfeeKMSExOblEOfPn00depU7dixQ1u2bFFZWZmOHj1abw+6pvZrrccee0yhoaG6ePFig/32\n7NnTqi0pDMNQZWWldu7cKR8fnxbFuBpXvR8AAADoRAy0ipq5pP53331nLF682AgNDTXc3NyMPn36\nGFOnTjWOHz9upKWlGRaLxZBk9OvXz/jggw+MtWvXGr6+voYkIzAw0Hj11VeNrVu3GoGBgYYkw8/P\nz3j99dcNwzCMzMxMY8yYMUZAQIDh5uZmXHfddcasWbOMkydPNjkHwzCM8vJy45e//KVx3XXXGT17\n9jRGjhxp/O53vzMkGSEhIcaRI0ea3G/Dhg1G3759DUmGxWIxJk+ebGzatMl2njfddJORn59vvPji\ni4aPj48hybjxxhttWzbs27fPuO666wxJto+7u7sxaNAgY+fOnbZz2r17t+Ht7W2sXr36mtf+jTfe\nMPr3728X62qfp556yjAMg/vRivvRVM39+QFbIHQ03A8AQBew3WQYhtH2pWfXZTKZtG3bNjbubiMZ\nGRk6ceKE1q9fb2urqqrSE088oYyMDJ0/f16enp5OzNC1OPp+8PPTfNOnT5ckZWVlOTkTSNwPAECX\nkMXqo+iwvvnmGyUlJdV7365Hjx4KDQ2V1WqV1WqlKGwn3A8AAICuiXcK0WF5enrK3d1dW7Zs0Zkz\nZ2S1WlVUVKSXXnpJv/vd75SQkODQ9//QMO4HAABA10RRiA7L19dXf/rTn/SPf/xDAwYMkKenpyIj\nI5WZmam1a9fqv/7rv5ydokvhfnQN7777rpYsWaLa2lpNmTJFoaGhMpvNCg4OVnx8vI4ePdqseI6K\n88OY69evV0xMzFWPr1q1SpGRkfLx8ZGHh4ciIiL0+OOPX3UBpNdee03Dhw+Xt7e3brzxRs2ZM0ff\nfPON7fibb76pZ599VjU1NS3OFwCAzo6iEB1abGys/vznP9tW8Lxw4YL+53/+R/Pnz5ebG08/tzfu\nR+e2fPlypaena+nSpaqtrdUHH3yg1157TSUlJTpw4IAqKys1atQoFRUVNTmmo+LUOXHihEaNGqWF\nCxfq0qVLV+2zb98+PfbYYyooKNC5c+eUmpqqtLQ02/t9dbZt26b7779f06dPV2FhoXJycrR//35N\nmDBB1dXVkqTJkyfLbDZr7NixunDhQrPzBQCgK6AoBOCyKisrrzkb1ZnGaIq1a9dq69at2r59u7y9\nvSVJ0dHRGjlypCwWi8LCwrRmzRqVlpbq5ZdfblZsR8U5cuSInnjiCc2bN09Dhgy5Zr+ePXvqkUce\nkb+/v7y9vTVjxgxNmTJFe/fu1enTp239/vCHP+j666/Xb3/7W/n6+mrIkCFauHChDh8+rIMHD9r6\nJScn69Zbb9XEiRNtxSIAAK6EohCAy9qyZYuKi4s7/RiNycvL07Jly7Ry5UqZzWZJkpubm9566y27\nfuHh4ZKk/Pz8Jsd2VBxJuvXWW7Vz507df//98vDwuGa/t99+W927d7dr6927tyTZzS6ePn1aQUFB\nMplMtrYbbrhBknTy5Em7769YsUKHDx9WWlpas3IGAKAroCgE0GkYhqF169Zp0KBB8vDwkJ+fn+65\n5x599tlntj5JSUnq0aOH+vbta2t79NFH5eXlJZPJpHPnzkmSUlJStGjRIuXn58tkMikiIkLp6eky\nm80KCAjQ3LlzFRQUJLPZrJiYGLuZpdaMIUl79+6Vj4+P1qxZ06bXq056eroMw9DkyZMb7FdZWSlJ\nrV4wyFFxmuOrr76Sp6enwsLCbG3h4eH1CvK69wnrCtc6fn5+Gj16tNLS0sROTQAAV0NRCKDTWLFi\nhZYsWaInn3xSxcXF2r9/v06fPq3Y2FidOXNG0pUC6If7Hm7atEkrV660a0tLS9OkSZPUv39/GYah\nvLw8JSUlKTExUZcuXVJycrIKCgp06NAhVVdX66677rI9mtiaMSTZFjWpra113MVpwK5duzRw4EBZ\nLJYG+3388ceSpJEjR7ZqPEfFaapLly5p3759evjhh9WjRw9b+9KlS/XNN99ow4YNKi8v1/Hjx5WW\nlqaf/exnGjFiRL04Q4cO1VdffaUjR460S94AAHQUFIUAOoXKykqtW7dO9957r2bPni1fX19FRUXp\nhRde0Llz5/Tiiy86bCw3NzfbbGRkZKQyMjJUXl6uzMxMh8SPi4tTWVmZli1b5pB4DamoqNCXX36p\n/v37X7PPmTNntHXrViUnJys6OrrRGcW2jtNcqampCgoK0urVq+3aR48ercWLFyspKUk+Pj665ZZb\nVF5erpdeeumqcW666SZJ0rFjx9o8ZwAAOhKKQgCdwvHjx3Xx4kUNGzbMrn348OHq0aOH3eOdjjZs\n2DBZLBa7x1Q7i+LiYhmG0eAsYXR0tJKTk3XPPfdoz549cnd3b9FYjorTHG+88Ya2b9+ud955x7aA\nTp0nn3xSL774ot577z1dvHhRX3zxhWJiYhQdHW23IE2dumtUN+sMAICroCgE0CnUbRfQs2fPesd6\n9eql8vLyNh3fw8NDZ8+ebdMx2sLly5clqcGFWwICArRv3z5t2LBBvr6+LR7LUXGaauvWrVq7dq1y\nc3PVr18/u2Nff/21nn32Wf3qV7/SHXfcIS8vL4WFhWnz5s0qKirS888/Xy+ep6enpH9fMwAAXAUb\niwHoFHr16iVJVy3+Lly4oJCQkDYb22q1tvkYbaWu0Gloc/Y+ffrYrm9rOCpOU2zYsEHvvPOO9u3b\nd9X/UXDixAnV1NTo+uuvt2v38fGRv7+/jh8/Xu87VVVVkv59zQAAcBUUhQA6hVtuuUU9e/bU3/72\nN7v2gwcPqqqqSrfddputzc3NTVar1WFj5+bmyjAMu8VJHD1GWwkICJDJZFJpaek1+/xwS4mWclSc\nhhiGoSeeeELnz59Xdna23Nyu/p+xugL+66+/tmsvLy9XSUmJbWuK76u7RoGBgQ7OGgCAjo3HRwF0\nCmazWYsWLdIbb7yhV155RWVlZTp27JjmzZunoKAgPfLII7a+ERERKikpUXZ2tqxWq86ePVtvXzpJ\n8vf3V1FRkQoKClReXm4r8mpra3X+/HlVV1fr6NGjSklJUWhoqBITEx0yxp49e9ptSwqLxaLw8HAV\nFhZe9XheXp4CAwM1c+bMescSEhIUGBioQ4cONTqOo+I05p///Keee+45bd68We7u7jKZTHaf3//+\n95KksLAwjRkzRps3b9b+/ftVWVmp06dP2/6ePPTQQ/Vi112jqKioVucJAEBnQlEIoNNYvny5UlNT\ntWrVKvXu3VujR49Wv379lJubKy8vL1u/+fPna8yYMZo1a5YGDhyop59+2vZI4PcXGZk3b54CAgIU\nGRmpiRMnqqSkRNKVd8qioqLk6emp2NhYDRgwQO+//77de3mtHaM9xcXF6fjx47b9A7+voT35qqqq\nVFxcrJycnEbHcEScjz76SCNHjtT111+vgwcP6siRIwoKCtJPf/pT7d+/v9Fxvs9kMikrK0sJCQl6\n6KGH5Ofnp8jISJ06dUo7d+5UbGxsve988sknCg4O1uDBg5s0BgAAXYXJYJfeVjGZTNq2bVu9PcsA\nNK4j/vzMnTtXWVlZ+vbbb52dylVNnz5dkpSVldXk7+Tl5WnQoEHKzMzU7Nmzm/y92tpa3X777UpM\nTNSDDz7Y7FwdHactffvttwoJCdHq1au1aNGiJn+vJfcDAIAOJouZQgD4gYYWZemMIiIitGrVKq1a\ntUoXL15s0ndqamqUnZ2t8vJyJSQktHhsR8VpaytWrNCQIUOUlJTk7FQAAGh3FIUA4AKWLFmi6dOn\nKyEhocFFZ+rk5uZq586d2rNnT4N7HLZXnLa0bt06HT58WLt3726XvRUBAOhoKAoB4P8sXbpUmZmZ\nKi0tVVhYmHbs2OHslBxqzZo1SkpK0jPPPNNo37Fjx+rVV19V3759WzWmo+K0lZycHH333XfKzc2V\nn5+fs9MBAMAp2JICAP5PamqqUlNTnZ1Gmxo3bpzGjRvn7DQ6jPj4eMXHxzs7DQAAnIqZQgAAAABw\nYRSFAAAAAODCKAoBAAAAwIVRFAIAAACAC2Pz+lYymUwaMWKEQkJCnJ0K0Ons2LGDn59m+uijjyRJ\nI0aMcHImkK7cjxEjRrB5PQCgM8uiKGyl6dOnOzsFwGm++eYbffrpp5owYYKzUwGcJjo6WgsXLnR2\nGgAAtBRFIYCW2759u2bOnCl+jQAAAHRaWbxTCAAAAAAujKIQAAAAAFwYRSEAAAAAuDCKQgAAAABw\nYRSFAAAAAODCKAoBAAAAwIVRFAIAAACAC6MoBAAAAAAXRlEIAAAAAC6MohAAAAAAXBhFIQAAAAC4\nMIpCAAAAAHBhFIUAAAAA4MIoCgEAAADAhVEUAgAAAIALoygEAAAAABdGUQgAAAAALoyiEAAAAABc\nGEUhAAAAALgwikIAAAAAcGEUhQAAAADgwigKAQAAAMCFURQCAAAAgAujKAQAAAAAF0ZRCAAAAAAu\njKIQAAAAAFwYRSEAAAAAuDCKQgAAAABwYRSFAAAAAODCKAoBAAAAwIVRFAIAAACAC6MoBAAAAAAX\n5ubsBAB0DlarVRcvXrRrq6iokCSdP3/ert1kMqlXr17tlhsAAABajqIQQJOUlJQoODhYNTU19Y75\n+/vb/XnMmDHat29fe6UGAACAVuDxUQBNEhgYqFGjRqlbt4Z/bZhMJs2aNaudsgIAAEBrURQCaLKf\n//znjfbp3r277r333nbIBgAAAI5AUQigyaZOnSo3t2s/dd69e3eNHz9e1113XTtmBQAAgNagKATQ\nZD4+PpowYcI1C0PDMDR79ux2zgoAAACtQVEIoFlmz5591cVmJKlHjx66++672zkjAAAAtAZFIYBm\nufvuu2WxWOq1u7u7a8qUKfLy8nJCVgAAAGgpikIAzWI2m3XvvffK3d3drt1qter+++93UlYAAABo\nKYpCAM123333yWq12rX5+PjorrvuclJGAAAAaCmKQgDNduedd9ptWO/u7q5Zs2apR48eTswKAAAA\nLUFRCKDZ3NzcNGvWLNsjpFarVffdd5+TswIAAEBLUBQCaJFZs2bZHiENDAzUyJEjnZwRAAAAWoKi\nEECLxMTEKDg4WJL0wAMPqFs3fp0AAAB0RlffgboFtm/f7qhQADqJ4cOH66uvvtJ1113H7wDAxdxw\nww2Kjo52dhoAAAcwGYZhOCSQyeSIMAAAoBOYNm2asrKynJ0GAKD1shw2UyhJ27Zt04wZMxwZEkAH\nt2PHDk2bNs3ZaTiVyWTi918zTZ8+XZIoKjqpuvsHAOgaeAkIQKu4ekEIAADQ2VEUAgAAAIALoygE\nAAAAABdGUQgAAAAALoyiEAAAAABcGEUhAAAAALgwikIA6CB2794tX19fvfXWW85OpUOaO3euTCaT\n7TN79ux6fd59910tWbJEtbW1mjJlikJDQ2U2mxUcHKz4+HgdPXq0WWM6Ks4PY65fv14xMTFXPb5q\n1SpFRkbKx8dHHh4eioiI0OOPP66LFy/W6/vaa69p+PDh8vb21o033qg5c+bom2++sR1/88039eyz\nz6qmpsbue9nZ2XbXsnfv3i0+HwBA50dRCAAdhGEYzk6hw/P399eePXv0+eefa8uWLXbHli9frvT0\ndC1dulS1tbX64IMP9Nprr6mkpEQHDhxQZWWlRo0apaKioiaP56g4dU6cOKFRo0Zp4cKFunTp0lX7\n7Nu3T4899pgKCgp07tw5paamKi0trd7egNu2bdP999+v6dOnq7CwUDk5Odq/f78mTJig6upqSdLk\nyZNlNps1duxYXbhwwfbd+Ph4FRYWav/+/Zo4cWKzzwMA0LVQFAJABxEXF6fS0lJNmjTJ2amosrLy\nmjNZzuTp6anx48drwIAB8vDwsLWvXbtWW7du1fbt2+Xt7S1Jio6O1siRI2WxWBQWFqY1a9aotLRU\nL7/8crPGdFScI0eO6IknntC8efM0ZMiQa/br2bOnHnnkEfn7+8vb21szZszQlClTtHfvXp0+fdrW\n7w9/+IOuv/56/fa3v5Wvr6+GDBmihQsX6vDhwzp48KCtX3Jysm699VZNnDjRViyaTCYFBwcrNjZW\nN910U7POAwDQ9VAUAgDq2bJli4qLi52dRpPk5eVp2bJlWrlypcxmsyTJzc2t3mO44eHhkqT8/Pwm\nx3ZUHEm69dZbtXPnTt1///12Be0Pvf322+revbtdW93jnd+fXTx9+rSCgoJkMplsbTfccIMk6eTJ\nk3bfX7FihQ4fPqy0tLRm5QwAcA0UhQDQARw4cEChoaEymUzauHGjJCkjI0NeXl6yWCzKycnRhAkT\n5OPjo5CQEL3++uu276anp8tsNisgIEBz585VUFCQzGazYmJi7GaMkpKS1KNHD/Xt29fW9uijj8rL\ny0smk0nnzp2TJKWkpGjRokXKz8+XyWRSRESEJGnv3r3y8fHRmjVr2uOSNFl6eroMw9DkyZMb7FdZ\nWSlJ8vHxadV4jorTHF999ZU8PT0VFhZmawsPD69XuNe9T1hXuNbx8/PT6NGjlZaWxmPKAIB6KAoB\noAMYOXKkPvzwQ7u2+fPna8GCBaqsrJS3t7e2bdum/Px8hYeH6+GHH5bVapV0pdhLTEzUpUuXlJyc\nrIKCAh06dEjV1dW66667bI8cpqena8aMGXZjbNq0SStXrrRrS0tL06RJk9S/f38ZhqG8vDxJsi1W\nUltb2ybXoKV27dqlgQMHymKxNNjv448/lnTlWreGo+I01aVLl7Rv3z49/PDD6tGjh6196dKl+uab\nb7RhwwaVl5fr+PHjSktL089+9jONGDGiXpyhQ4fqq6++0pEjR9olbwBA50FRCACdQExMjHx8fNSn\nTx8lJCSooqJCp06dsuvj5uamQYMGycPDQ5GRkcrIyFB5ebkyMzMdkkNcXJzKysq0bNkyh8RzhIqK\nCn355Zfq37//NfucOXNGW7duVXJysqKjoxudUWzrOM2VmpqqoKAgrV692q599OjRWrx4sZKSkuTj\n46NbbrlF5eXleumll64ap+7dwWPHjrV5zgCAzoWiEAA6mbrZorqZwmsZNmyYLBaLPvvss/ZIyymK\ni4tlGEaDs4TR0dFKTk7WPffcoz179sjd3b1FYzkqTnO88cYb2r59u9555x3bAjp1nnzySb344ot6\n7733dPHiRX3xxReKiYlRdHS03YI0dequ0ZkzZ9o8bwBA50JRCABdmIeHh86ePevsNNrM5cuXJanB\nhVsCAgK0b98+bdiwQb6+vi0ey1Fxmmrr1q1au3atcnNz1a9fP7tjX3/9tZ599ln96le/0h133CEv\nLy+FhYVp8+bNKioq0vPPP18vnqenp6R/XzMAAOq4OTsBAEDbsFqtunDhgkJCQpydSpupK3R+uDn7\n9/Xp00e9evVq9ViOitMUGzZs0DvvvKN9+/apZ8+e9Y6fOHFCNTU1uv766+3afXx85O/vr+PHj9f7\nTlVVlaR/XzMAAOpQFAJAF5WbmyvDMOwWHXFzc2v0sdPOJCAgQCaTSaWlpdfs88MtJVrKUXEaYhiG\nnnjiCZ0/f17Z2dlyc7v6f6brCv2vv/7arr28vFwlJSW2rSm+r+4aBQYGOjhrAEBnx+OjANBF1NbW\n6vz586qurtbRo0eVkpKi0NBQJSYm2vpERESopKRE2dnZslqtOnv2bL097STJ399fRUVFKigoUHl5\nuaxWq/bs2dPhtqSwWCwKDw9XYWHhVY/n5eUpMDBQM2fOrHcsISFBgYGBOnToUKPjOCpOY/75z3/q\nueee0+bNm+Xu7i6TyWT3+f3vfy9JCgsL05gxY7R582bt379flZWVOn36tB555BFJ0kMPPVQvdt01\nioqKanWeAICuhaIQADqAjRs3avjw4ZKkxYsXKz4+XhkZGVq/fr0kafDgwfriiy+0efNmLVq0SJI0\nfvx4nThxwhbj8uXLioqKkqenp2JjYzVgwAC9//77du/bzZ8/X2PGjNGsWbM0cOBAPf3007bHCb+/\nQMm8efMUEBCgyMhITZw4USUlJe1yHVoiLi5Ox48ft+0f+H0N7clXVVWl4uJi5eTkNDqGI+J89NFH\nGjlypK6//nodPHhQR44cUVBQkH76059q//79jY7zfSaTSVlZWUpISNBDDz0kPz8/RUZG6tSpU9r5\n/9m707CojvRt4HfL1myNoLKLsoiRuEfzFxSXOGrUKG4oRhMxTiKa+VgOPAAAIABJREFUDJhoomKM\nShQ1JsIl6hiXcTIxLiwOxD2jDEEnbolBDY4OYFARFRRkEYQG6v3gS8eWrRsaGuj7d118oE6dqqer\nz6H74ZxTFRMDb2/vKvtcvHgRDg4O6Nmzp0p9EBGR7uDto0REzcAHH3yADz74oEr5/PnzlX6vXKOw\nOubm5jVeMatkZWWF+Pj4KuVffPGF0u99+vRBenq6Utno0aORn59fa/va8Je//AVbt25FTEwMZs6c\nqbStS5cuNc62GR0djaFDh6JTp0519qGJdgYMGIAzZ87UWqd79+4qJ4bt2rVDWFiY4h8HtXn06BFO\nnTqF1atXQyKRqNQ+ERHpDl4pJCJqJWqbbKW1KC4uxokTJ5CSkqKYOMXNzQ0hISEICQlBYWGhSu2U\nl5cjNjYWBQUF8PPzq3c8mmqnsa1cuRK9e/dGYGAggGdXJDMzM3HmzBmkpqZqOToiItK2ZpcUlpSU\nICgoCLa2tjAxMcHx48e1HVKtvvzyS8VEB9u2bdN2OM1OQ8bHz8+vyvM0Nf0cPny4kV6B5sTExMDF\nxaXW11E57by2j6uTJ09iypQp6NixI4yMjGBmZoaXX34ZH374YbXPn6nixddva2tb5aoOUV1ycnLw\n+uuvw93dHe+8846ifOnSpfD19YWfn1+tk85USkhIQExMDI4dO1brGodN1U5j2rhxI5KSknD06FHF\n2opxcXFwcHCAt7c3jhw5ouUIiYhI25pdUvjVV1/h+PHjuH79OsLDw1X+r6+2LFq0CD/99JO2w2i2\nGjo+P/zwAx4/fgy5XK6YZW/8+PEoLS3FkydPkJWVVeOtdM3N5MmTcfPmTbi6usLCwgJCCAghUFZW\nhqKiIjx48EDxpVKbx9WSJUswYsQIyGQyHDp0CHl5ecjMzMTGjRtx+vRp9OzZs9rbD+vy4uu/f/8+\n9uzZ0wivQPcEBwdj9+7dyMvLg7OzM6Kjo7UdUqPYtm2b4rwRQlQ5ftasWYPAwECsXbu2zraGDx+O\n7777Dra2tg2KSVPtNJa4uDiUlJQgISEBlpaWivIJEyYojeXDhw+1GCUREWmb1p4pLC4uxvDhw6t8\n8Y2NjUW/fv3Qtm1bvPfee1qKjpoDiUSCgQMHVvnvu0QigYGBAQwMDGBiYoJXXnlFSxFqhp6eHoyN\njWFsbAx3d3etxhIXF6dYEPvrr79WlEulUowaNQoDBw7EK6+8gqlTp+LGjRto166dFqOlSqGhoQgN\nDdV2GM3CyJEjMXLkSG2H0Wz4+PjAx8dH22EQEVEzp7Urhbt27UJWVlaV8oyMDMXtLaTb9u3bp9Lt\nWHPnzsUbb7zRBBE1vtjYWK32Xznd/aefflrtdjMzM3z00Ud49OgRdu7c2ZShEREREVEj0UpSuGDB\nAixcuBBpaWmQSCRwc3PDv/71L7i5ueHevXv45ptvIJFIYGZmpla7Qghs3LgR3bp1g5GRESwtLTFh\nwgRcv35dUWfTpk2QSqWwtrZGQEAA7OzsIJVK4eXlhfPnz2vsNZ4+fRoeHh6wsLCAVCpFjx49cOLE\nCQDAn//8Z8VzVa6urvj1118BALNnz4aJiQksLCzw/fffA3g2icFnn30GJycnGBsbo2fPnjhw4ACA\nZ7MFmpiYwNzcHFlZWVi4cCEcHBxw48YNlWIMDw+Hqakp2rRpg1deeQU2NjYwMDCAqakp+vbtC29v\nb3Ts2BFSqRRt27bFJ598otg3MDAQhoaGSrdMvf/++zA1NYVEIqnzVqTjx49rfL2z2sZq69atMDU1\nhYmJCeLi4jB69GjIZDI4Ojpi3759Su38+OOPePXVV2FiYgKZTIYePXooZlxU5Rhr6PtSG1X679ev\nn+L46tmzp2KJgRetXLkSVlZWkEqlWL16NYqKinDu3Dk4OTlVu/B1JU9PTwDAv/71LwCNe061hPOI\niIiIqMUTGgJAHDhwQOX6kydPFq6urlXKbWxsxKxZs+oVw2effSYMDQ3Ft99+Kx4/fiyuXLki+vbt\nK9q3by/u37+vqDd37lxhamoqrl27Jp4+fSqSk5NF//79hbm5ubh9+7ba/aakpAgA4q9//auiLCoq\nSqxcuVLk5OSIR48eiQEDBoh27doptk+ePFno6emJu3fvKrX15ptviu+//17x+6JFi4SRkZGIjo4W\nubm5Ijg4WLRp00ZcvHhRCCHEsmXLBAARFBQkIiIixKRJk8R///tflWNfsWKFACDOnz8vnjx5Ih4+\nfChef/11AUAcOXJEZGdniydPnojAwEABQCQlJSn2nTFjhrCxsVFqb8OGDQKAyM7OrnV8Dh8+LMzN\nzUVISIjKsd67d08AED4+PtVuV3WsTp06JfLy8kRWVpbw9vYWpqamorS0VAghRGFhoZDJZGL9+vWi\nuLhY3L9/X0yaNEnxelQ9xmp7X1xdXYWFhYVS7KdOnRIbNmxQKqtu3FTtf+DAgaJjx46ioqJCUXbo\n0CHh7u6u1MemTZvEmjVrhBBC/Pe//xUARL9+/Wp9Hx48eCAACGdnZ0WZOudUda+/Ji3lPFL37x8J\nMWXKFDFlyhRth0H1xPePiKhViWw1SWFRUZEwMzMTfn5+SuUXLlwQAJSSj7lz51b5Unrx4kUBQKxa\ntUrtvqv78v6i0NBQAUBkZWUJIYQ4efKkACBWr16tqJOXlye6dOkiysrKhBBCFBcXCxMTE6XXVFRU\nJIyMjMT8+fOFEH98mS0uLlY7biH+SAoLCgoUZd98840AIK5evaooqxzH/fv3K8oakhTWR21JYX3H\nasuWLQKASE1NFUII8dtvvwkA4vDhw1X6UOcYq+19cXV1FQCq/NSVFKrT/44dOwQAER8fryibMmWK\nACB++uknRdnAgQPFrVu3hBB/nAOvvfZalZifV1JSIgCI9u3bK8rUOafUSQpf1FzPIyaF6mNS0bLx\n/SMialUiW83i9cnJySgsLES/fv2Uyvv37w9DQ8M6b2Pr168fTExMlG7D06TK5yQr1xF77bXX4O7u\njr/97W8IDg6GRCLB/v374efnBz09PQDAjRs3UFRUhO7duyvaMTY2hq2tbaPFCQCGhoYAgLKysirx\ny+XyRuu3Ieo7VpWvtfJ1ubi4wNraGjNnzkRQUBD8/f0Vy0Q09Bh7noWFBR4/fqz4PSEhAT///HOt\n+6jT/7Rp0xAUFIR//OMfGDZsGHJzc5GWlgYjIyP84x//gKenJ9LT02FoaAgnJycAzxY+B6AUV3Vy\ncnIAADKZrNZ6jXFONefzKCwsDFFRURprr7U7d+4cAMDX11fLkVB9nDt3DgMGDNB2GEREpCHNbkmK\n+qr8Ilvdc4ht27ZFQUFBnW0YGRkhOztbI/EcOXIEQ4cORYcOHWBkZKT0PB7wbAbNgIAA3Lx5E6dO\nnQIA/OMf/8CcOXMUdZ48eQLg2aQfz69ld+vWLRQVFWkkztZCU2NlbGyM+Ph4DBo0CGvWrIGLiwv8\n/PxQXFyskWOsJkOHDsWiRYtqraNO/+bm5pg0aRJiYmJQVFSEffv2Yc6cORg3bhwOHDiAkpIS7Nu3\nT2mdwE6dOsHAwAAPHjyoNY779+8DALp06VLn62roOcXziIiIiKjxtZorhW3btgWAar+YP378GI6O\njrXuL5fLVaqnitu3b2PixImYNGkS/va3v8He3h4RERFVvtD6+/sjODgYO3fuRMeOHSGTydCpUyfF\n9g4dOgB4dgViwYIFDY6rNdPkWL388ss4dOgQsrOzsXHjRqxbtw4vv/wyRo8eDaD+x1hDqXuMz549\nG3v27ME///lP7Nu3D7GxsYo17A4fPozY2FjFZDHAs2UnvL29ER8fj99//x3Ozs7VxnHmzBkAwKhR\no2qNtz7nVGJiIn755Rd8+OGHLe48+vDDDzF16tRGa7+1qbxCyKurLROv8BIRtS6t5kph9+7dYWZm\nVuUWvPPnz6O0tLTOtewSEhIghNDI7TBXr16FXC7H/Pnz4eLiAqlUColEUqWepaUlpk2bhtjYWHz5\n5ZdVFmGvnPkzKSmpwTE1Bn19/WZzO6mmxiozMxPXrl0D8CyZWLt2Lfr27Ytr1641+BhrKHX7HzZs\nGDp16oTVq1fD2toa7dq1w6hRo2BnZ4cVK1bA2dm5yi2gS5YsAQCEhIRUG0N+fj7CwsJgbW2Nd955\np9Z463NO/fLLLzA1NQWgO+cRERERkbZpLSm0srJCZmYm0tPTUVBQ0ODkQiqVYuHChTh48CD27NmD\n/Px8XL16FfPmzYOdnR3mzp2rVL+iogK5ubkoKyvDlStXsGDBAjg5OcHf379BcQBQPKN18uRJPH36\nFCkpKTU+bzZv3jyUlJTg8OHDGDduXJXXNHv2bOzbtw9bt25Ffn4+ysvLkZGRgXv37jU4zoZyc3ND\nTk4OYmNjIZfLkZ2djVu3bqm077FjxzS6JIWmxiozMxMBAQG4fv06SktL8euvv+LWrVsYMGCA2seY\npqnbv0QiwaxZs3D9+nXMmjULAKCnp4e33noLycnJeOutt6r0MWLECKxduxbffPMN/P39cfnyZTx9\n+hT5+fn44YcfFM8nRkdHw8LCQmnfhpxTcrkcDx48QEJCgiIp1JXziIiIiEjrNDVlDdScfe/SpUui\nU6dOwtjYWAwaNEicP39e9OnTRwAQ+vr6om/fviI6OlqtGCoqKsSGDRtEly5dhIGBgbC0tBQTJ04U\nN27cUKo3d+5cYWBgIBwcHIS+vr6QyWRiwoQJIi0tTa3+hBDiq6++EjY2NgKAMDU1FZMmTRJCCLF4\n8WJhZWUl2rZtK3x9fcXmzZsFAOHq6lpliv4+ffqIpUuXVtt+SUmJWLx4sXBychL6+vqiQ4cOYvLk\nySI5OVmsX79eGBsbCwCiY8eO4ttvv1Ur9vDwcGFiYiIAiM6dO4vTp0+LdevWCQsLCwFA2NjYiO++\n+07s379f8RotLS3Fvn37hBBCPHr0SAwbNkxIpVLh7Ows/vKXv4iPP/5YABBubm7i9u3bNY7P0aNH\nhbm5udKskTXJz88XgwcPFlZWVgKAaNOmjXBzc1MspaDKWG3ZskXxWrt06SLS0tLE9u3bhUwmEwBE\np06dxP/+9z+Rnp4uvLy8hKWlpdDT0xP29vZi2bJlipksVTnGanpf/vOf/wh3d3fFbKO2trZi+PDh\n1b7mmsZN1WO80s2bN4W1tbViyQ0hni09YW1tLeRyeY1jfvbsWfHmm28KJycnYWhoKExNTUX37t3F\nwoULRUZGRpX6qpxTBw8erHHm1ed/Dh48qNinJZxHQnD20frg7JUtG98/IqJWJVIihBCaSC4lEgkO\nHDjQIp6pCQgIQFRUFB49eqTtUAAAY8eOxebNm2t8hououWsO55Q2z6OW9PevueAzhS0b3z8iolYl\nqtU8U6iuyintteH5W2WvXLkCqVTKhJBavKY+p3geEREREWlGs04Kr1+/rjSFfE0/fn5+LarfxYsX\nIyUlBf/73/8we/ZsfP755y0mdqLmojHPI2qeAgIClP6OPb+kSqWTJ09i6dKlqKiowMSJE+Hk5ASp\nVAoHBwf4+PjgypUravWpqXZebDMsLAxeXl7Vbg8JCYGHhwdkMhmMjIzg5uaGTz75BIWFhVXq7t27\nF/3794e5uTk6deqE2bNnK5aNAYDvv/8e69evr/JPm9jYWKWxbN++fb1fDxERtXzNOil86aWXIISo\n82f//v0qtxkcHIzdu3cjLy9PMT1/U/T7PBMTE7z00kv405/+hJUrV8LDw6Ne7VSnsWMnepEq51Rj\naMzziJovKysrHDt2DDdu3MCuXbuUtq1YsQKbNm1CcHAwKioqcPr0aezduxc5OTk4c+YMiouLMXjw\nYGRmZqrcn6baqZSSkoLBgwfjo48+qnGdzPj4eHzwwQdIT0/Hw4cPERoaivDw8CrLQBw4cAAzZsyA\nr68vMjIyEBcXh8TERIwePRplZWUAgPHjx0MqlWL48OGKtU4BwMfHBxkZGUhMTMSYMWPUfh1ERNTK\naOrpRHCiBSLSUdr++1dUVCQ8PT1bVB/1mahk7ty5wsHBodpta9euFe7u7qK4uFgIIYRcLhdvvPGG\nUp0LFy4IAFUmqaqNptoRQoikpCQxadIksWfPHtG7d2/Rq1evauuNHTtWMblVpalTpwoAShMsDRs2\nTNjb24uKigpFWeVkTGfOnFHaPzAwUHh6elY7wVRQUJBo166dWq+FE80QEbUqkc36SiEREdVt165d\nyMrKavF91FdqaiqWL1+OVatWQSqVAni2juqhQ4eU6rm4uAAA0tLSVG5bU+0AQK9evRATE4MZM2bA\nyMioxnqHDx+Gnp6eUlnl7Z3PX128c+cO7OzslNbv7NixIwBUWR5o5cqVSEpKQnh4uFoxExGRbmBS\nSETUxIQQ2LhxI7p16wYjIyNYWlpiwoQJuH79uqJOYGAgDA0NYWtrqyh7//33YWpqColEgocPHwIA\nFixYgIULFyItLQ0SiQRubm7YtGkTpFIprK2tERAQADs7O0ilUnh5eSmt9diQPgDg+PHjGl1vtL42\nbdoEIQTGjx9fa73i4mIAgEwma1B/mmpHHXfv3oWxsbHSZEouLi5VEvXK5wkrE9dKlpaWGDJkCMLD\nwyE0M+k4ERG1IkwKiYia2MqVK7F06VIsW7YMWVlZSExMxJ07d+Dt7Y0HDx4AeJbovLjExZYtW7Bq\n1SqlsvDwcIwbNw6urq4QQiA1NRWBgYHw9/dHUVERgoKCkJ6ejkuXLqGsrAwjRozAnTt3GtwH8MeM\nsxUVFZobnHo4cuQIunbtChMTk1rrXbhwAQAwaNCgBvWnqXZUVVRUhPj4eLz77rswNDRUlAcHB+P+\n/fuIiIhAQUEBkpOTER4ejlGjRmHAgAFV2unTpw/u3r2Ly5cvN0ncRETUcjApJCJqQsXFxdi4cSMm\nTZqEmTNnwsLCAj169MC2bdvw8OFDbN++XWN96evrK65Genh4YOvWrSgoKMDu3bs10v7YsWORn5+P\n5cuXa6S9+njy5Al+//13uLq61ljnwYMH2L9/P4KCguDp6VnnFcXGbkddoaGhsLOzw+rVq5XKhwwZ\ngsWLFyMwMBAymQzdu3dHQUEBdu7cWW07Xbp0AQBcvXq10WMmIqKWhUkhEVETSk5ORmFhIfr166dU\n3r9/fxgaGird3qlp/fr1g4mJidJtqi1dVlYWhBC1XiX09PREUFAQJkyYgGPHjsHAwKBefWmqHXUc\nPHgQkZGROHHiBMzNzZW2LVu2DNu3b8epU6dQWFiImzdvwsvLC56enoqrwc+rHKPKq9FERESVmBQS\nETWhymUBzMzMqmxr27YtCgoKGrV/IyMjZGdnN2ofTenp06cAUOvELdbW1oiPj0dERAQsLCzq3Zem\n2lHV/v37sW7dOiQkJKBz585K2+7du4f169fjvffew2uvvQZTU1M4Oztjx44dyMzMxIYNG6q0Z2xs\nDOCPMSMiIqqkr+0AiIh0Sdu2bQGg2uTv8ePHcHR0bLS+5XJ5o/fR1CoTnRcXZ39ehw4dFOPeEJpq\nRxURERE4ceIE4uPjq/0HQkpKCsrLy2Fvb69ULpPJYGVlheTk5Cr7lJaWAvhjzIiIiCoxKSQiakLd\nu3eHmZkZfv75Z6Xy8+fPo7S0FK+88oqiTF9fH3K5XGN9JyQkQAihNAmJpvtoatbW1pBIJMjLy6ux\nzotLStSXptqpjRACS5YsQW5uLmJjY6GvX/3HdGVif+/ePaXygoIC5OTkKJameF7lGNnY2Gg4aiIi\naul4+ygRUROSSqVYuHAhDh48iD179iA/Px9Xr17FvHnzYGdnh7lz5yrqurm5IScnB7GxsZDL5cjO\nzq6y/hwAWFlZITMzE+np6SgoKFAkeRUVFcjNzUVZWRmuXLmCBQsWwMnJCf7+/hrp49ixY1pfksLE\nxAQuLi7IyMiodntqaipsbGwwbdq0Ktv8/PxgY2ODS5cu1dmPptqpy7Vr1/DFF19gx44dMDAwgEQi\nUfr58ssvAQDOzs4YNmwYduzYgcTERBQXF+POnTuK42fOnDlV2q4cox49ejQ4TiIial2YFBIRNbEV\nK1YgNDQUISEhaN++PYYMGYLOnTsjISEBpqaminrz58/HsGHDMH36dHTt2hWff/654ta/5ycTmTdv\nHqytreHh4YExY8YgJycHwLNnx3r06AFjY2N4e3vD3d0d//73v5Wev2toH83B2LFjkZycrFg/8Hm1\nrclXWlqKrKwsxMXF1dmHJto5d+4cBg0aBHt7e5w/fx6XL1+GnZ0dBg4ciMTExDr7eZ5EIkFUVBT8\n/PwwZ84cWFpawsPDA7dv30ZMTAy8vb2r7HPx4kU4ODigZ8+eKvVBRES6g7ePEhE1MYlEgkWLFmHR\nokW11rOyskJ8fHyV8i+++ELp9z59+iA9Pb1KPXNz8xqvoGmij9GjRyM/P7/W9pvCX/7yF2zduhUx\nMTGYOXOm0rYuXbrUONtmdHQ0hg4dik6dOtXZhybaGTBgAM6cOVNrne7du6ucGLZr1w5hYWEICwur\ns+6jR49w6tQprF69GhKJRKX2iYhId/BKIRFRK1Xb5CstVXFxMU6cOIGUlBTFxClubm4ICQlBSEgI\nCgsLVWqnvLwcsbGxKCgogJ+fX73j0VQ7jW3lypXo3bs3AgMDATy7IpmZmYkzZ84gNTVVy9EREZG2\nMSkkIqIWIycnB6+//jrc3d3xzjvvKMqXLl0KX19f+Pn51TrpTKWEhATExMTg2LFjta5x2FTtNKaN\nGzciKSkJR48eVaytGBcXBwcHB3h7e+PIkSNajpCIiLSNSSERUSsTHByM3bt3Iy8vD87OzoiOjtZ2\nSBqxbds2CCEUP3v27FHavmbNGgQGBmLt2rV1tjV8+HB89913sLW1bVBMmmqnscTFxaGkpAQJCQmw\ntLRUlE+YMEFpLB8+fKjFKImISNv4TCERUSsTGhqK0NBQbYehFSNHjsTIkSO1HUaz4ePjAx8fH22H\nQUREzRyvFBIREREREekwJoVEREREREQ6jEkhERERERGRDmNSSEREREREpMOYFBIREREREekwiRBC\naKQhiUQTzRAREVELMGXKFERFRWk7DCIiargojS1JceDAAU01RUQtxNmzZxEeHs7zn0gHdezYUdsh\nEBGRhmjsSiER6Z7IyEhMmzYN/DNCRERE1GJF8ZlCIiIiIiIiHcakkIiIiIiISIcxKSQiIiIiItJh\nTAqJiIiIiIh0GJNCIiIiIiIiHcakkIiIiIiISIcxKSQiIiIiItJhTAqJiIiIiIh0GJNCIiIiIiIi\nHcakkIiIiIiISIcxKSQiIiIiItJhTAqJiIiIiIh0GJNCIiIiIiIiHcakkIiIiIiISIcxKSQiIiIi\nItJhTAqJiIiIiIh0GJNCIiIiIiIiHcakkIiIiIiISIcxKSQiIiIiItJhTAqJiIiIiIh0GJNCIiIi\nIiIiHcakkIiIiIiISIcxKSQiIiIiItJhTAqJiIiIiIh0GJNCIiIiIiIiHcakkIiIiIiISIcxKSQi\nIiIiItJhTAqJiIiIiIh0GJNCIiIiIiIiHcakkIiIiIiISIcxKSQiIiIiItJhTAqJiIiIiIh0mL62\nAyCiliE7Oxv//Oc/lcp+/vlnAMD27duVys3NzTF9+vQmi42IiIiI6k8ihBDaDoKImr+SkhJYW1uj\nsLAQenp6AIDKPx8SiURRTy6XY9asWfj73/+ujTCJiIiISD1RvH2UiFRiZGSEKVOmQF9fH3K5HHK5\nHGVlZSgrK1P8LpfLAQBvvvmmlqMlIiIiIlUxKSQilb355psoLS2ttU7btm3x2muvNVFERERERNRQ\nTAqJSGXDhg1Dhw4datxuYGCAmTNnQl+fjysTERERtRRMColIZW3atMGMGTNgYGBQ7Xa5XM4JZoiI\niIhaGCaFRKSW6dOnK54dfJG9vT08PT2bOCIiIiIiaggmhUSklldffRWdOnWqUm5oaIhZs2YpzURK\nRERERM0fk0IiUttbb71V5RbS0tJS3jpKRERE1AIxKSQitc2YMaPKLaRubm7o0aOHliIiIiIiovpi\nUkhEanvppZfg4eGhuFXUwMAAs2fP1nJURERERFQfTAqJqF7efvtt6OnpAQDKysp46ygRERFRC8Wk\nkIjqZfr06SgvLwcA9O3bF87OzlqOiIiIiIjqg0khEdWLk5MT/u///g8AMGvWLC1HQ0RERET1pf9i\nwdmzZ7Fx40ZtxEJELUxJSQkkEgl++OEHJCYmajscImoBoqKitB0CERG9oMqVwjt37iA6OlobsRBR\nC+Po6AgbGxtIpVJth9LqnTt3DufOndN2GC1KRkYGP8+aEb4fRETNV5UrhZX4nzwiUkVqairc3Ny0\nHUar5+vrC4B/m9URGRmJadOmccyaicr3g4iImh8+U0hEDcKEkIiIiKhlY1JIRERERESkw5gUEhER\nERER6TAmhURERERERDqMSSEREREREZEOY1JIRKRDjh49CgsLCxw6dEjboTR7J0+exNKlS1FRUYGJ\nEyfCyckJUqkUDg4O8PHxwZUrV9RqT1PtvNhmWFgYvLy8qt0eEhICDw8PyGQyGBkZwc3NDZ988gkK\nCwur1N27dy/69+8Pc3NzdOrUCbNnz8b9+/cV27///nusX78e5eXl9Y6XiIiaJyaFREQ6RAih7RBa\nhBUrVmDTpk0IDg5GRUUFTp8+jb179yInJwdnzpxBcXExBg8ejMzMTJXb1FQ7lVJSUjB48GB89NFH\nKCoqqrZOfHw8PvjgA6Snp+Phw4cIDQ1FeHi4YomTSgcOHMCMGTPg6+uLjIwMxMXFITExEaNHj0ZZ\nWRkAYPz48ZBKpRg+fDgeP36sdrxERNR8MSkkItIhY8eORV5eHsaNG6ftUFBcXFzjFS5tWrduHfbv\n34/IyEiYm5sDADw9PTFo0CCYmJjA2dkZa9asQV5eHv7+97+r1bam2rl8+TKWLFmCefPmoXfv3jXW\nMzMzw9y5c2FlZQVzc3NMnToVEydOxPHjx3Hnzh1Fva+//hqa9OGqAAAgAElEQVT29vb4+OOPYWFh\ngd69e+Ojjz5CUlISzp8/r6gXFBSEXr16YcyYMYpkkYiIWj4mhUREpBW7du1CVlaWtsNQkpqaiuXL\nl2PVqlWQSqUAAH19/Sq327q4uAAA0tLSVG5bU+0AQK9evRATE4MZM2bAyMioxnqHDx+Gnp6eUln7\n9u0BQOnq4p07d2BnZweJRKIo69ixIwDg1q1bSvuvXLkSSUlJCA8PVytmIiJqvpgUEhHpiDNnzsDJ\nyQkSiQSbN28GAGzduhWmpqYwMTFBXFwcRo8eDZlMBkdHR+zbt0+x76ZNmyCVSmFtbY2AgADY2dlB\nKpXCy8tL6UpSYGAgDA0NYWtrqyh7//33YWpqColEgocPHwIAFixYgIULFyItLQ0SiQRubm4AgOPH\nj0Mmk2HNmjVNMSRVbNq0CUIIjB8/vtZ6xcXFAACZTNag/jTVjjru3r0LY2NjODs7K8pcXFyqJOiV\nzxNWJq6VLC0tMWTIEISHh/N2ZCKiVoJJIRGRjhg0aBB++uknpbL58+fjww8/RHFxMczNzXHgwAGk\npaXBxcUF7777LuRyOYBnyZ6/vz+KiooQFBSE9PR0XLp0CWVlZRgxYoTiVsRNmzZh6tSpSn1s2bIF\nq1atUioLDw/HuHHj4OrqCiEEUlNTAUAxiUlFRUWjjEFdjhw5gq5du8LExKTWehcuXADwbEwbQlPt\nqKqoqAjx8fF49913YWhoqCgPDg7G/fv3ERERgYKCAiQnJyM8PByjRo3CgAEDqrTTp08f3L17F5cv\nX26SuImIqHExKSQiIgCAl5cXZDIZOnToAD8/Pzx58gS3b99WqqOvr49u3brByMgIHh4e2Lp1KwoK\nCrB7926NxDB27Fjk5+dj+fLlGmlPHU+ePMHvv/8OV1fXGus8ePAA+/fvR1BQEDw9Peu8otjY7agr\nNDQUdnZ2WL16tVL5kCFDsHjxYgQGBkImk6F79+4oKCjAzp07q22nS5cuAICrV682esxERNT4mBQS\nEVEVlVeRKq8U1qRfv34wMTHB9evXmyKsRpWVlQUhRK1XCT09PREUFIQJEybg2LFjMDAwqFdfmmpH\nHQcPHkRkZCROnDihmECn0rJly7B9+3acOnUKhYWFuHnzJry8vODp6ak0IU2lyjF68OBBo8dNRESN\nj0khERE1iJGREbKzs7UdRoM9ffoUAGqduMXa2hrx8fGIiIiAhYVFvfvSVDuq2r9/P9atW4eEhAR0\n7txZadu9e/ewfv16vPfee3jttddgamoKZ2dn7NixA5mZmdiwYUOV9oyNjQH8MWZERNSy6Ws7ACIi\narnkcjkeP34MR0dHbYfSYJWJTm2Ls3fo0AFt27ZtcF+aakcVEREROHHiBOLj42FmZlZle0pKCsrL\ny2Fvb69ULpPJYGVlheTk5Cr7lJaWAvhjzIiIqGVjUkhERPWWkJAAIYTSZCT6+vp13nbaHFlbW0Mi\nkSAvL6/GOi8uKVFfmmqnNkIILFmyBLm5uYiNjYW+fvUf+ZUJ/b1795TKCwoKkJOTo1ia4nmVY2Rj\nY6PhqImISBt4+ygREamsoqICubm5KCsrw5UrV7BgwQI4OTnB399fUcfNzQ05OTmIjY2FXC5HdnZ2\nlbXuAMDKygqZmZlIT09HQUEB5HI5jh07prUlKUxMTODi4oKMjIxqt6empsLGxgbTpk2rss3Pzw82\nNja4dOlSnf1oqp26XLt2DV988QV27NgBAwMDSCQSpZ8vv/wSAODs7Ixhw4Zhx44dSExMRHFxMe7c\nuYO5c+cCAObMmVOl7cox6tGjR4PjJCIi7WNSSESkIzZv3oz+/fsDABYvXgwfHx9s3boVYWFhAICe\nPXvi5s2b2LFjBxYuXAgAeP3115GSkqJo4+nTp+jRoweMjY3h7e0Nd3d3/Pvf/1Z6Dm/+/PkYNmwY\npk+fjq5du+Lzzz9X3Gb4/MQl8+bNg7W1NTw8PDBmzBjk5OQ0yTjUZuzYsUhOTlasH/i82tbkKy0t\nRVZWFuLi4ursQxPtnDt3DoMGDYK9vT3Onz+Py5cvw87ODgMHDkRiYmKd/TxPIpEgKioKfn5+mDNn\nDiwtLeHh4YHbt28jJiYG3t7eVfa5ePEiHBwc0LNnT5X6ICKi5k0iXvjUiIyMxLRp07ggLRFRM+Lr\n6wsAiIqK0loMAQEBiIqKwqNHj7QWgzrq83mWmpqKbt26Yffu3Zg5c6bK+1VUVGDo0KHw9/fHO++8\nU59wNdpOY3r06BEcHR2xevVqxT8PVMHvF0REzVYUrxQSEZHKapuEpTVwc3NDSEgIQkJCUFhYqNI+\n5eXliI2NRUFBAfz8/Ordt6baaWwrV65E7969ERgYqO1QiIhIQ5gUEhERPWfp0qXw9fWFn59frZPO\nVEpISEBMTAyOHTtW6xqHTdVOY9q4cSOSkpJw9OjRJllbkYiImkaLSwqPHj0KCwuLOmduU7VeY/ny\nyy8VM9lt27ZNKzE0pj//+c8wNzeHRCJBUlKSolzb416diooKhIWFwcvLq95txMTEwMXFRWmSBgMD\nAzg4OGDGjBn473//q8GIn2nJx3p14/Xiz4trpWlbSzqmtSE4OBi7d+9GXl4enJ2dER0dre2QGtWa\nNWsQGBiItWvX1ll3+PDh+O6772Bra9ugPjXVTmOJi4tDSUkJEhISYGlpqe1wiIhIg1pcUqjqswja\nfmZh0aJF+Omnn7QaQ2PauXMnduzYUaVc2+P+opSUFAwePBgfffQRioqK6t3O5MmTcfPmTbi6usLC\nwgJCCDx+/Bjbtm3DmTNn8Oqrr+LGjRsajLxlH+vVjZcQAmVlZSgqKsKDBw+a3ZWQlnJMa0toaChK\nSkoghMDvv/+OKVOmaDukRjdy5EisW7dO22E0Gz4+Pli6dCn09PS0HQoREWlYi1uncOzYsVVu5yku\nLsbw4cOVvphWV48aX3Ma98uXLyMkJATz5s3DkydPNP7l3tTUFOPGjUN5eTkmTpyIiIgIbN68WWPt\nt8ZjXU9PD8bGxjA2Noa7u7u2w1FJSxpfIiIiovpocVcKq7Nr1y5kZWVpOwydI5FIGr0PIQSioqKw\nfft2tfft1asXYmJiMGPGDKXp8jXt1VdfBQD89ttvjdZHpdZ0rMfGxmo7hCqa+zFNRERE1BganBRu\n2rQJUqkU1tbWCAgIgJ2dHaRSKby8vHD+/HmlukIIbNy4Ed26dYORkREsLS0xYcIEXL9+Xanejz/+\niFdffRUmJiaQyWTo0aMH8vPzcebMGTg5OUEikSiuyCxYsAALFy5EWloaJBIJ3Nzcqq2nav9bt26F\nqakpTExMEBcXh9GjR0Mmk8HR0RH79u1TivP06dPw8PCAhYUFpFIpevTogRMnTjR0SAE8m4Xus88+\ng5OTE4yNjdGzZ08cOHBA7RgB4Ntvv0W/fv0glUphamqKzp074/PPP1frPRFCYMOGDejatSuMjIxg\nYWGBjz/+WKlOdeOuTqzl5eUIDQ1F165dYWxsjPbt28PZ2RmhoaGYOnWqRsa1OsePH2/QYtllZWUA\noJR48lhXD49pIiIiIi0SLzhw4ICoprhWc+fOFaampuLatWvi6dOnIjk5WfTv31+Ym5uL27dvK+p9\n9tlnwtDQUHz77bfi8ePH4sqVK6Jv376iffv24v79+0IIIQoLC4VMJhPr168XxcXF4v79+2LSpEki\nOztbCCHEnTt3BAARERGhaHfy5MnC1dVVKabq6qnSvxBCLFu2TAAQp06dEnl5eSIrK0t4e3sLU1NT\nUVpaqqgXFRUlVq5cKXJycsSjR4/EgAEDRLt27RTbU1JSBADx17/+Va3xFEKIRYsWCSMjIxEdHS1y\nc3NFcHCwaNOmjbh48aJaMYaFhQkAYu3ateLRo0ciJydHfP3112LGjBlqj4lEIhFfffWVyM3NFUVF\nRWLLli0CgPj1119rHXdVY12zZo3Q09MTcXFxoqioSPzyyy/CxsZGDB06VO3xe9H//d//iV69elW7\n7fDhw8Lc3FyEhITU2Y6rq6uwsLBQKvv2228FAPHxxx8rynis1zxeQUFB4urVq1XGlsd07aZMmSKm\nTJmi9n66rD6fZ9R4+H4QETVbkRpLCl/84nfx4kUBQKxatUoIIURRUZEwMzMTfn5+SvUuXLggACi+\nkP/2228CgDh8+HC1fdX3i7Kq/Qvxxxe+4uJiRVnll8XU1NQaxyE0NFQAEFlZWUKI+ieFxcXFwsTE\nRCnWoqIiYWRkJObPn69yjKWlpaJt27Zi2LBhSu2XlZWJ8PBwlcekqKhImJiYiBEjRijV27dvn1pf\noOsaz/79+4tXX31VqY/33ntPtGnTRpSUlKgwcjWrLSlUx/NJTmFhoYiOjhY2NjbC2tpaZGRkCCF4\nrL84XgCq/NSWFPKYrh6TQvUxCWle+H4QETVbkY020Uy/fv1gYmKiuGUrOTkZhYWF6Nevn1K9/v37\nw9DQUHGrqYuLC6ytrTFz5kwEBQXB399fI1PXq9p/TQwNDQEAcrm8xjqVazY1dHHnGzduoKioCN27\nd1eUGRsbw9bWtsotcLXFeOXKFTx+/BijRo1Sqqenp4egoCD8/PPPKo1JamoqioqKMHz48Aa9rtpi\nBYCnT59CKpUq1SsvL4eBgUGzmu0uLy8PEokEenp6sLW1xZgxY7BixQo4ODgA4LH+IgsLCzx+/Fjx\n+4IFC+rcp6ZYdP2Yjo6ObpLnHlsbjhkREVHtGnX2USMjI2RnZwOA4kuhmZlZlXpt27ZFQUEBgGfJ\nT3x8PJYsWYI1a9YgJCQEU6dOxe7du2FsbFzvWFTtXx1HjhzBhg0bkJycjPz8/Fq/RKvjyZMnAIBP\nP/0Un376qdI2Ozs7ldvJz88H8Oz1VUfVMcnIyAAAdOjQQeW+62PMmDHYsGED4uLiMHLkSCQnJyM2\nNhZvvPFGs0oKX0xyXsRjvXbh4eH13lfXj+kBAwbgww8/bIRIW6ezZ88iPDxc8Tw2aVfl+0FERM1P\noyWFcrkcjx8/hqOjI4A/vsRV94X0+XoA8PLLL+PQoUPIzs7Gxo0bsW7dOrz88stYvnx5veNRp39V\n3L59GxMnTsSkSZPwt7/9Dfb29oiIiMAnn3xS7xgrVX5RDQsLU+uqyovs7e0BAA8fPqx2u6pjUnml\no6SkpN6xqGLlypX45Zdf4O/vj8LCQtjZ2WHq1Kn1ngBGW3isNx5dP6YdHR05QY2awsPDOWbNCJNC\nIqLmqdGWpEhISIAQAgMGDAAAdO/eHWZmZvj555+V6p0/fx6lpaV45ZVXAACZmZm4du0agGfJ0dq1\na9G3b19FWX2p2r+qrl69Crlcjvnz58PFxQVSqVRjtyh17NgRUqkUSUlJDWqnc+fOsLKywg8//FDt\ndlXHpHv37mjTpg1+/PHHBsVTl+TkZKSlpSE7OxtyuRy3b9/G1q1bYWlp2aj9ahqPddXcu3cPs2fP\nVmsfHtNEREREmqexpLCiogK5ubkoKyvDlStXsGDBAjg5OcHf3x/As//ML1y4EAcPHsSePXuQn5+P\nq1evYt68ebCzs8PcuXMBPPuiHBAQgOvXr6O0tBS//vorbt26pUguq2NlZYXMzEykp6ejoKCg2lvb\nVO1fVU5OTgCAkydP4unTp0hJSanzWS1VSaVSzJ49G/v27cPWrVuRn5+P8vJyZGRk4N69eyq3Y2Rk\nhODgYCQmJiIwMBB3795FRUUFCgoKcO3aNZXHpEOHDpg8eTKio6Oxa9cu5Ofn48qVKxpfZ+2DDz6A\nk5MTCgsLNdpuXY4dO9agJSlexGO9dkIIFBcXIyYmBjKZTK19eUwTERERNYIXp56p7+yjBgYGwsHB\nQejr6wuZTCYmTJgg0tLSlOpVVFSIDRs2iC5duggDAwNhaWkpJk6cKG7cuKGok56eLry8vISlpaXQ\n09MT9vb2YtmyZaKsrExEREQIW1tbAUCYmJiI8ePHCyGEuHTpkujUqZMwNjYWgwYNEp9++mm19VTp\nf8uWLcLExEQAEF26dBFpaWli+/btQiaTCQCiU6dO4n//+58QQojFixcLKysr0bZtW+Hr6ys2b94s\nAAhXV1exYMECYWNjIwAIU1NTMWnSJLXGtKSkRCxevFg4OTkJfX190aFDBzF58mSRnJysVoxCCLF5\n82bRo0cPIZVKhVQqFX369BFbtmxReUyEEKKgoED8+c9/Fu3atRNmZmZi0KBB4rPPPhMAhKOjo7h8\n+XK17486scbHx4t27dopzVJpYGAgunXrJmJiYtQaPyGEOHv2rBg4cKCws7NTtGdrayu8vLzEjz/+\nqKh39OhRYW5uLlavXl1jW//5z3+Eu7u7oh07Ozvh6+tbY31dP9YPHjxY48yjz/98+umnascihG4e\n05x9VH2c7bJ54ftBRNRsRUqEEOL5JDEyMhLTpk3DC8W1CggIQFRUFB49eqTyPkQv2rp1K1JSUhAW\nFqYoKy0txZIlS7B161bk5uY2aAIWoqamyWPa19cXABAVFdUosbZG9fk8o8bD94OIqNmK0thEMw1d\nhoF02/379xEYGFjlOUpDQ0M4OTlBLpdDLpczKaQWg8c0ERERtRSNNtEMKbt+/TokEkmdP35+ftoO\nVSuMjY1hYGCAXbt24cGDB5DL5cjMzMTOnTvx2Wefwc/PD5mZmRxDajFUOabVfaaSmtbJkyexdOlS\nVFRUYOLEiXBycoJUKoWDgwN8fHxw5coVtdrTVDsvthkWFgYvL69qt4eEhMDDwwMymQxGRkZwc3PD\nJ598Uu1zrnv37kX//v1hbm6OTp06Yfbs2bh//75i+/fff4/169fzn8BERK1Qg5PC4OBg7N69G3l5\neXB2dkZ0dLQm4mp1XnrpJQgh6vzZv3+/tkPVCgsLC/zwww/47bff4O7uDmNjY3h4eGD37t1Yt24d\nvvnmG44htSiqHNPUfK1YsQKbNm1CcHAwKioqcPr0aezduxc5OTk4c+YMiouLMXjwYGRmZqrcpqba\nqZSSkoLBgwfjo48+QlFRUbV14uPj8cEHHyA9PR0PHz5EaGgowsPDFbcjVzpw4ABmzJgBX19fZGRk\nIC4uDomJiRg9ejTKysoAAOPHj4dUKsXw4cNrXauViIhaoBefMuSD4EREzU9zmGimqKhIeHp6tpg+\n6vt5tnbtWuHu7i6Ki4uFEELI5XLxxhtvKNW5cOGCACDWrFmjcruaakcIIZKSksSkSZPEnj17RO/e\nvUWvXr2qrTd27FhRVlamVDZ16lQBQNy+fVtRNmzYMGFvby8qKioUZZUTSp05c0Zp/8DAQOHp6Snk\ncrlaMfP7BRFRsxXJ20eJiEglu3btQlZWVovvozapqalYvnw5Vq1aBalUCgDQ19fHoUOHlOq5uLgA\nANLS0lRuW1PtAECvXr0QExODGTNmwMjIqMZ6hw8fhp6enlJZ+/btAUDp6uKdO3dgZ2entAZpx44d\nAQC3bt1S2n/lypVISkriQvRERK0Ik0IiolZKCIGNGzeiW7duMDIygqWlJSZMmIDr168r6gQGBsLQ\n0BC2traKsvfffx+mpqaQSCR4+PAhAGDBggVYuHAh0tLSIJFI4Obmhk2bNkEqlcLa2hoBAQGws7OD\nVCqFl5eX0lqWDekDAI4fP67RtURrs2nTJgghMH78+FrrFRcXA0CDnwvVVDvquHv3LoyNjeHs7Kwo\nc3FxqZKMVz5PWJm4VrK0tMSQIUMQHh7OmUSJiFoJJoVERK3UypUrsXTpUixbtgxZWVlITEzEnTt3\n4O3tjQcPHgB4lgRNnTpVab8tW7Zg1apVSmXh4eEYN24cXF1dIYRAamoqAgMD4e/vj6KiIgQFBSE9\nPR2XLl1CWVkZRowYgTt37jS4D+CP2a0rKio0Nzg1OHLkCLp27QoTE5Na6124cAEAMGjQoAb1p6l2\nVFVUVIT4+Hi8++67MDQ0VJQHBwfj/v37iIiIQEFBAZKTkxEeHo5Ro0ZhwIABVdrp06cP7t69i8uX\nLzdJ3ERE1LiYFBIRtULFxcXYuHEjJk2ahJkzZ8LCwgI9evTAtm3b8PDhQ2zfvl1jfenr6yuuRnp4\neGDr1q0oKCjA7t27NdL+2LFjkZ+fj+XLl2ukvZo8efIEv//+O1xdXWus8+DBA+zfvx9BQUHw9PSs\n84piY7ejrtDQUNjZ2WH16tVK5UOGDMHixYsRGBgImUyG7t27o6CgADt37qy2nS5dugAArl692ugx\nExFR42NSSETUCiUnJ6OwsBD9+vVTKu/fvz8MDQ2Vbu/UtH79+sHExETpNtWWICsrC0KIWq8Senp6\nIigoCBMmTMCxY8dgYGBQr7401Y46Dh48iMjISJw4cQLm5uZK25YtW4bt27fj1KlTKCwsxM2bN+Hl\n5QVPT0/FFd/nVY5R5RVnIiJq2ZgUEhG1QpVLBpiZmVXZ1rZtWxQUFDRq/0ZGRsjOzm7UPjTt6dOn\nAFDrxC3W1taIj49HREQELCws6t2XptpR1f79+7Fu3TokJCSgc+fOStvu3buH9evX47333sNrr70G\nU1NTODs7Y8eOHcjMzMSGDRuqtGdsbAzgjzEjIqKWTV/bARARkea1bdsWAKpN/h4/fgxHR8dG61su\nlzd6H42hMtGpbXH2Dh06KMa2ITTVjioiIiJw4sQJxMfHV/tPgpSUFJSXl8Pe3l6pXCaTwcrKCsnJ\nyVX2KS0tBfDHmBERUcvGpJCIqBXq3r07zMzM8PPPPyuVnz9/HqWlpXjllVcUZfr6+pDL5RrrOyEh\nAUIIpQlKNN1HY7C2toZEIkFeXl6NdV5cUqK+NNVObYQQWLJkCXJzcxEbGwt9/eo/8iuT93v37imV\nFxQUICcnR7E0xfMqx8jGxkbDURMRkTbw9lEiolZIKpVi4cKFOHjwIPbs2YP8/HxcvXoV8+bNg52d\nHebOnauo6+bmhpycHMTGxkIulyM7O7vK2nQAYGVlhczMTKSnp6OgoECR5FVUVCA3NxdlZWW4cuUK\nFixYACcnJ/j7+2ukj2PHjjXJkhQmJiZwcXFBRkZGtdtTU1NhY2ODadOmVdnm5+cHGxsbXLp0qc5+\nNNVOXa5du4YvvvgCO3bsgIGBASQSidLPl19+CQBwdnbGsGHDsGPHDiQmJqK4uBh37txRHCNz5syp\n0nblGPXo0aPBcRIRkfYxKSQiaqVWrFiB0NBQhISEoH379hgyZAg6d+6MhIQEmJqaKurNnz8fw4YN\nw/Tp09G1a1d8/vnnitsCn59oZN68ebC2toaHhwfGjBmDnJwcAM+eK+vRoweMjY3h7e0Nd3d3/Pvf\n/1Z6Nq+hfTSVsWPHIjk5WbF+4PNqW5OvtLQUWVlZiIuLq7MPTbRz7tw5DBo0CPb29jh//jwuX74M\nOzs7DBw4EImJiXX28zyJRIKoqCj4+flhzpw5sLS0hIeHB27fvo2YmBh4e3tX2efixYtwcHBAz549\nVeqDiIiaN4l44VMjMjIS06ZN44K0RETNiK+vLwAgKipKy5EoCwgIQFRUFB49eqTtUKqoz+dZamoq\nunXrht27d2PmzJkq71dRUYGhQ4fC398f77zzTn3C1Wg7jenRo0dwdHTE6tWrsXDhQpX34/cLIqJm\nK4pXComIqEFqm5ilpXFzc0NISAhCQkJQWFio0j7l5eWIjY1FQUEB/Pz86t23ptppbCtXrkTv3r0R\nGBio7VCIiEhDmBQSERE9Z+nSpfD19YWfn1+tk85USkhIQExMDI4dO1brGodN1U5j2rhxI5KSknD0\n6NEmWVuRiIiaBpNCIiKql+DgYOzevRt5eXlwdnZGdHS0tkPSmDVr1iAwMBBr166ts+7w4cPx3Xff\nwdbWtkF9aqqdxhIXF4eSkhIkJCTA0tJS2+EQEZEGcUkKIiKql9DQUISGhmo7jEYzcuRIjBw5Utth\nNBs+Pj7w8fHRdhhERNQIeKWQiIiIiIhIhzEpJCIiIiIi0mFMComIiIiIiHQYk0IiIiIiIiIdVuNE\nM5GRkU0ZBxER1SIjIwMA/zar4+zZswA4Zs1F5ftBRETNj0QIIZ4viIyMxLRp07QVDxEREbViL3zt\nICIi7YuqkhQSEamq8p9I/DNCRERE1GJF8ZlCIiIiIiIiHcakkIiIiIiISIcxKSQiIiIiItJhTAqJ\niIiIiIh0GJNCIiIiIiIiHcakkIiIiIiISIcxKSQiIiIiItJhTAqJiIiIiIh0GJNCIiIiIiIiHcak\nkIiIiIiISIcxKSQiIiIiItJhTAqJiIiIiIh0GJNCIiIiIiIiHcakkIiIiIiISIcxKSQiIiIiItJh\nTAqJiIiIiIh0GJNCIiIiIiIiHcakkIiIiIiISIcxKSQiIiIiItJhTAqJiIiIiIh0GJNCIiIiIiIi\nHcakkIiIiIiISIcxKSQiIiIiItJhTAqJiIiIiIh0GJNCIiIiIiIiHcakkIiIiIiISIcxKSQiIiIi\nItJhTAqJiIiIiIh0GJNCIiIiIiIiHcakkIiIiIiISIcxKSQiIiIiItJhTAqJiIiIiIh0GJNCIiIi\nIiIiHaav7QCIqGXIyMjArFmzUF5erijLzc2Fubk5hg4dqlS3a9eu+Prrr5s4QiIiIiKqDyaFRKQS\nR0dH3Lp1C2lpaVW2/fjjj0q/Dx48uKnCIiIiIqIG4u2jRKSyt99+GwYGBnXW8/Pza4JoiIiIiEgT\nmBQSkcpmzJiBsrKyWuu8/PLL8PDwaKKIiIiIiKihmBQSkcpcXV3Rs2dPSCSSarcbGBhg1qxZTRwV\nERERETUEk0IiUsvbb78NPT29areVlZXB19e3iSMiIiIiooZgUkhEapk+fToqKiqqlLdp0wYDBgxA\n586dmz4oIiIiIqo3JoVEpBY7OzsMHDgQbdoo//lo06YN3n77bS1FRURERET1xaSQiNT21ltvVSkT\nQmDSpElaiIaIiIiIGoJJIRGpbcqUKUrPFerp6eFPf/oTrK2ttRgVEREREdUHk0IiUpulpSVGjBih\nSAyFEJg5c6aWoyIiIiKi+mBSSET1MnPmTMWEMwYGBl0xX/QAACAASURBVJgwYYKWIyIiIiKi+mBS\nSET1Mn78eBgZGQEAxo0bBzMzMy1HRERERET1waSQiOrF1NRUcXWQt44SERERtVwSIYTQdhC6KDIy\nEtOmTdN2GERE9P811sch/94TEVFzUs3nXZS+NgKhPxw4cEDbIRDVW3l5ORYtWgQnJyd8+OGH2g6n\nxTh79izCw8N5/jcTle9HY+P7TdS8hIWFAQA/v9TAz6+WrbbPOyaFWjZ16lRth0DUIFFRUdDT0+Ox\nrKbw8HCOWTPSFEkh32+i5iUqKgoAz0118fOrZavp847PFBJRgzy/XiERERERtTxMComIiIiIiHQY\nk0IiIiIiIiIdxqSQiIiIiIhIhzEpJCIiIiIi0mFMComoWTh69CgsLCxw6NAhbYfS7J08eRJLly5F\nRUUFJk6cCCcnJ0ilUjg4OMDHxwdXrlxRqz1NtfNim2FhYfDy8qp2e0hICDw8PCCTyWBkZAQ3Nzd8\n8sknKCwsrFJ379696N+/P8zNzdGpUyfMnj0b9+/fV2z//vvvsX79epSXl9c7XiKi+uLnV+0CAgIg\nkUgUPzNnzqxSR9Ofa8+r6/NIG+3V9Tpr+lyLjY1VGsv27ds36LU8j0khETULjbVweGuzYsUKbNq0\nCcHBwaioqMDp06exd+9e5OTk4MyZMyguLsbgwYORmZmpcpuaaqdSyv9j777jorrS/4F/RtrMAENR\nqYpSRCWiJmoiCjFqMJZVY0MSs7uWKLYvGI0BNMYSJbZFgkoi6JqoiZRgwNiwEIKuirHiYkTARhFQ\nqVIH5vz+8MesI20GZuYO8LxfL/65c+5znnsY5uHMvffctDS8++67WL58OcrLyxtsEx8fj6VLl+Lh\nw4d49uwZAgICEBQUhBkzZsi0i4iIwKxZszBjxgxkZWUhNjYWiYmJGDduHGpqagAAkyZNAp/Px+jR\no1FUVKRwvoQQ0hpUv5pnamqKkydPIjU1Ffv27ZN5TRV1rY489YiLeM0dZ2N1bfLkycjKykJiYiLG\njx/f6uN5FU0KCSEaYcKECSguLsbEiRO5TgUVFRVK+0ZRmTZv3ozw8HBERkbC0NAQAODi4gJXV1cI\nhULY2tpi06ZNKC4uxg8//KBQbGXFuXXrFvz8/LBo0SIMHDiw0XYGBgbw8vKCqakpDA0N4eHhgSlT\npuDUqVPIzMyUttuzZw+srKywcuVKGBkZYeDAgVi+fDlu3ryJpKQkaTsfHx8MGDAA48ePl04WCSFE\nHah+NU8gEGDs2LFwdHSEnp6edLsq65q89YireM0dZ0N1jcfjwdraGm5ubujVq1erc3gVTQoJIeQ1\n+/btQ35+PtdpyEhPT8eaNWuwfv168Pl8AIC2tna9y5Xs7OwAABkZGXLHVlYcABgwYACio6Mxa9Ys\nmcL/umPHjtV7xmXdZTCvfvuamZkJS0tL8Hg86bbu3bsDAB49eiSz/7p163Dz5k21PIieEEI0kSbW\nr8aosq4B8tcjLuLJe5zqrGs0KSSEcO7ChQuwsbEBj8fDrl27AAAhISHQ19eHUChEbGwsxo0bB5FI\nhG7duuHw4cPSfYODg8Hn82FmZoaFCxfC0tISfD4fw4YNkzmT5O3tDV1dXVhYWEi3LVmyBPr6+uDx\neHj27BkAYNmyZVixYgUyMjLA4/Hg4OAAADh16hREIhE2bdqkjiGpJzg4GIwxTJo0qcl2FRUVAACR\nSNSq/pQVRxHZ2dkQCASwtbWVbrOzs6v3D07d/YR1BbSOiYkJRowYgaCgILqcixCiFlS/Wk7ddU3T\nNXSc6qxrNCkkhHDO1dUVFy9elNm2ePFifPbZZ6ioqIChoSEiIiKQkZEBOzs7zJ8/H2KxGMDLYjl7\n9myUl5fDx8cHDx8+xPXr11FTUwN3d3fppYjBwcHw8PCQ6WP37t1Yv369zLagoCBMnDgR9vb2YIwh\nPT0dAKQ3e0skEpWMQXOOHz+O3r17QygUNtnuypUrAF6OaWsoK468ysvLER8fj/nz50NXV1e6fdWq\nVcjNzcXOnTtRWlqKlJQUBAUF4YMPPsDQoUPrxXnzzTeRnZ2NW7duqSVvQkjHRvWr5dRd1zRdY8ep\nrrpGk0JCiMYbNmwYRCIRunbtCk9PT5SVleHx48cybbS1tdG3b1/o6enByckJISEhKC0txf79+5WS\nw4QJE1BSUoI1a9YoJZ4iysrK8ODBA9jb2zfaJi8vD+Hh4fDx8YGLi0uz37yqOo6iAgICYGlpiY0b\nN8psHzFiBHx9feHt7Q2RSIR+/fqhtLQUe/fubTBO3T0Wt2/fVnnOhBDSnI5evxqjzrqm6Zo7TnXV\nNW2VRieEECWrO4tU901rYwYPHgyhUIi7d++qIy2Vys/PB2OsyW9TXVxcUFZWBg8PD2zcuBE6Ojot\n6ktZcRRx5MgRREZG4vTp09KFBuqsXr0ae/fuxblz5/DOO+8gPz8ffn5+cHFxwcWLF6X3F9apG6O8\nvDyV500IIYroiPWrMeqsa5quueNUV12jSSEhpN3S09PD06dPuU6j1SorKwGgyRvbzczMsG/fPrzx\nxhut6ktZceQVHh6OwMBAJCQkwMrKSua1J0+eYMuWLfD398eoUaMAALa2tggLC4OJiQm2bduG4OBg\nmX0EAgGA/40ZIYS0Re2lfjVGnXVN0zV3nOqqazQpJIS0S2KxGEVFRejWrRvXqbRaXUFo6uHsXbt2\nhbGxcav7UlYceezcuRNxcXGIj4+HgYFBvdfT0tJQW1tbb7IoEolgamqKlJSUevtUV1cD+N+YEUJI\nW9Oe6ldj1FnXNF1zx6muukaTQkJIu5SQkADGmMxiJNra2s1etqOJzMzMwOPxUFxc3Gib15e2bill\nxWkKYwx+fn4oLCxETEwMtLUbLkV1/xA9efJEZntpaSkKCgrqXToKQDpG5ubmSs6aEELUoz3Vr8ao\ns65puuaOU111jRaaIYS0CxKJBIWFhaipqUFycjKWLVsGGxsbzJ49W9rGwcEBBQUFiImJgVgsxtOn\nT+s96w4ATE1NkZOTg4cPH6K0tBRisRgnT57kbElvoVAIOzs7ZGVlNfh6eno6zM3NMXPmzHqveXp6\nwtzcHNevX2+2H2XFac6dO3ewdetWhIWFQUdHBzweT+Zn+/btAF5eKjpy5EiEhYUhMTERFRUVyMzM\nhJeXFwBg3rx59WLXjZGzs3Or8ySEEHVoz/WrMeqqa/LgMl5Tx1lHXXWNJoWEEM7t2rULQ4YMAQD4\n+vpi8uTJCAkJwY4dOwAA/fv3x/379xEWFoYVK1YAAMaOHYu0tDRpjMrKSjg7O0MgEMDNzQ2Ojo74\n/fffZe5XWLx4MUaOHImPPvoIvXv3xtdffy29HMPFxUW6/PeiRYtgZmYGJycnjB8/HgUFBWoZh6ZM\nmDABKSkp0ucYvaqpZxdVV1cjPz8fsbGxzfahjDiXL1+Gq6srrKyskJSUhFu3bsHS0hLDhw9HYmJi\ns/28isfjISoqCp6enpg3bx5MTEzg5OSEx48fIzo6Gm5ubvX2+fPPP2FtbY3+/fvL1QchhLQG1a+W\nU3Vdk6cecRmvueOso7a6xggnIiIiGA0/aQ+mT5/Opk+fzmkOXl5ezNTUlNMcFNGSv/+0tDSmra3N\nDh48qNB+tbW1zM3Nje3bt0+h/VQVR5WePXvG+Hw+2759u0L7qfrzmD7vCdFMVL8U15LPMy8vL2Zt\nbV1vO9d1rS3Ea6qu+fj4sM6dOysUr4nfXySdKSSEtAtN3azeHjg4OGDDhg3YsGEDXrx4Idc+tbW1\niImJQWlpKTw9PVvct7LiqNq6deswcOBAeHt7c50KIYTIrb3XLwCoqKhAXFwc0tLSpAuncFnX2kq8\n1+saYww5OTm4cOEC0tPTWx3/VTQpbMM+/fRTGBoagsfj4ebNm1ynw6kNGzbAyckJIpEIenp6cHBw\nwBdffCH3h8yroqOjYWdnV+8+J11dXZiZmeG9997Dtm3bUFhYqIIjIaRx/v7+mDFjBjw9PZu8Ob9O\nQkICoqOjcfLkySafBaWuOKoUGBiImzdv4sSJE+32WVbKIG/daKzdiRMnYGRk1GEWgHjdN998AyMj\nI6XV3cuXL6Nv377o1KkTeDwezM3NsXHjRiVkqjyv10QLCwt88sknXKdF2piCggKMHTsWjo6OmDt3\nrnQ7V3WtLcRrqK7FxsbC2toabm5uOH78eKvzldGic5mk1ZR1OdHhw4cZAHbjxg0lZNV2jRgxgu3e\nvZs9f/6clZSUsIiICKajo8PGjh3b4pj29vbMyMiIMcaYRCJhhYWF7Pfff2ezZ89mPB6PWVpasj//\n/FNZh9BmcX35jb+/P9PV1WUAWM+ePVlUVBRnucirtX//cXFxzNfXV4kZtW0xMTEsICCA1dTUtGj/\njnb5qLx1o6F2x44dYyKRiB09elTVaWosVdTdDz74gAFghYWFSoupbK/WxPaC6pfiVPV5RnVNVmvr\nWmPo8lHSJlRUVGDYsGEt2tfAwABeXl4wNTWFoaEhPDw8MGXKFJw6dUp683Vr8Hg8GBsb47333sP+\n/fsRGRmJvLw8TJgwQa5vtjRda8aeawEBAaiqqgJjDA8ePMD06dO5TknlxowZg82bN3OdhsaYPHky\n/P39oaWlxXUq7V7dZ97EiRO5ToWoSFuuB21NR6xfjaG6JouLukaTwjaOx+NxnYLS7Nu3D/n5+S3a\n99ixY/X+cLp06QIAKC8vb3Vur5s+fTpmz56N/Px8fP/990qPr26tGXtCSNsib91QR31hjCEqKgqh\noaEq74vIh+oBIR0TTQrbEMYYtm3bht69e0NPTw9GRkZYuXKlTJutW7dCKBTC0NAQ+fn5WLFiBayt\nrZGamgrGGAIDA9G3b1/o6enBxMQEH374Ie7evSvdPzg4GHw+H2ZmZli4cCEsLS3B5/MxbNgwJCUl\n1cunuXje3t7Q1dWFhYWFdNuSJUugr68PHo+HZ8+eAQCWLVuGFStWICMjAzweDw4ODq0er+zsbAgE\nAtja2kq3nTp1SmnP6ql7ftDJkycB0NgTQpRDkc+C8+fPw8nJCUZGRuDz+XB2dkZcXJz0dXnqhrzt\nLly4ABsbG/B4POzatQsAEBISAn19fQiFQsTGxmLcuHEQiUTo1q0bDh8+LLN/bW0tAgIC0Lt3bwgE\nAnTp0gW2trYICAiAh4eHQmMUFBQEfX19dOrUCYMGDYK5uTl0dHSgr6+Pt956C25ubujevTv4fD6M\njY3xxRdfKDRuf/zxB95++20IhUKIRCI4OzujpKSkwVzy8vLQs2dPaGtrY+zYsdLtrak38o6rvO8V\nddeDpsb3008/ld6faG9vjxs3bgAA5syZA6FQCCMjIxw9ehTAy/fMV199BRsbGwgEAvTv3x8REREA\nmq65hJAWUOqFqkRuLbkme/Xq1YzH47F//etfrLCwkJWXl7Pdu3fXu7dh9erVDADz8fFhO3fuZFOn\nTmV//fUX++qrr5iuri47ePAgKyoqYsnJyeytt95iXbp0Ybm5udL9vby8mL6+Prtz5w6rrKxkKSkp\nbMiQIczQ0JA9fvxY2k7eeLNmzWLm5uYyx7Jt2zYGgD19+lS6bdq0acze3l6hMWlMWVkZMzQ0ZN7e\n3jLbjx07xgwNDdmGDRuajdHc/RMlJSUMAOvevbt0W0cce67vyWiLNO0es45OE+8plPezICoqiq1b\nt44VFBSw58+fs6FDh8osUa5I3ZCnXWZmJgPAdu7cKbMvAHbu3DlWXFzM8vPzmZubG9PX12fV1dXS\ndps2bWJaWlosNjaWlZeXs2vXrjFzc3P23nvvKTQ2ddauXcsAsKSkJFZWVsaePXvGxo4dywCw48eP\ns6dPn7KysjLm7e3NALCbN2/KNW4vXrxgIpGIbdmyhVVUVLDc3Fw2depU6Wfm6/cUVldXs2nTprHY\n2FiZ/BSpNw3dUyjvuMr7XmltPVDknsLm3pfTpk1jWlpaLDs7W2a/jz/+WOZ+1c8//5zp6emxX375\nhRUWFrJVq1axTp06Se/nb6zmyovql+KofrVtTd1TSL9Vjij6R1VeXs6EQiFzd3eX2d7QDe91H5IV\nFRUy+xsYGDBPT0+Z/a9cucIAyBQtLy+veh/8f/75JwPA1q9fr3A8LiaFq1evZo6OjqykpKTFMeQp\ngDwejxkbG8v029HGnoqq4qioahZNnRQ291nQkICAAAaA5efny103FKkvTU0KX/3cq5tQpqenS7cN\nGTKEvf322zJ9LFiwgHXq1IlVVVU1NyT11E0KS0tLpdt+/PFHBoDdvn1buq3uszE8PLzRWK+O23//\n+18GgB07dqzBtq+Oi1gsZh999BE7efKkwvm/qqlJYXPjKu97RZ2Twte9Or6MMXb27FkGgG3cuFHa\npri4mPXq1Uu6sEZFRQUTCoUyta68vJzp6emxxYsXM8YaHiNFUP1SHNWvtq2pSaG2Ms42EtVLT09H\neXk5Ro8e3aL9U1JS8OLFCwwePFhm+5AhQ6Crq1vvkqTXDR48GEKhUHp5YmvjqdKRI0cQGRmJ06dP\nw9DQUGX9lJWVgTEGkUjUZLuOMPZZWVmIjIxUe79t1aVLlwCAxkxD1P0+NN3rnwUNqVu2vLa2Vu66\n0dr60hBdXV0AgFgslm6rrKwEn8+XaVdbWwsdHR2lLaZQ129NTY10W92YvJrL614dNzs7O5iZmeGT\nTz6Bj48PZs+ejZ49e9bbp7a2Fh9//DGsrKxkLhtVpYbGtSHyvFfU6dXxBYBRo0bB0dER//73v7Fq\n1SrweDyEh4fD09NT+l5ITU1FeXk5+vXrJ40jEAhgYWGh1OOi+qUYql9tW1P1jiaFbURWVhYAoGvX\nri3av6ioCMDLVTpfZ2xsjNLS0mZj6Onp4enTp0qLpwrh4eEIDAxEQkICrKysVNrXvXv3AAB9+vRp\nsl1HGPvLly9j5syZau+3raMxI4p69bMAAI4fP45t27YhJSUFJSUlMpMFeetGa+uLvMaPH49t27Yh\nNjYWY8aMQUpKCmJiYvC3v/1N7SvHNjVuAoEA8fHx8PPzw6ZNm7BhwwZ4eHhg//79EAgE0nZLly5F\nZWUljh49igULFsDJyUmtx9Cc198r6tTU+AIvFzFauHAhli9fjnPnzuH999/HgQMH8NNPP0nblJWV\nAQC+/PJLfPnllzL7W1paKi1Xql8tQ2PW/tBCM21E3berVVVVLdrf2NgYABqcMBQVFaFbt25N7i8W\ni2XatTaeKuzcuROHDh1CfHy8yieEwMtFBABg3LhxTbbrCGM/ffp0MMboR86fuoUSuM6DfmR/H5ru\n9c+Cx48fY8qUKbCwsEBSUhKKi4uxZcsWaXt560Zr64u81q1bh1GjRmH27NkQiUSYOnUqPDw8EBYW\nptJ+X9fcuAHAG2+8gd9++w05OTnw9fVFREQEtm/fLtPGw8MDZ86cgbGxMf7xj3/InJ3k2uvvFVVL\nTEzEjh07AMg3vsDLxdr4fD727t2L1NRUiEQi9OjRQ/p63ZcUO3bsqPc3q8yz+1S/WvZ5yXUe9NO6\n319DaFLYRvTr1w+dOnXCH3/80eL9DQwMcPXqVZntSUlJqK6uxqBBg5rcPyEhAYwxDB06VOF42tra\nzV7q0hqMMfj6+uL27duIiYlp8AyasuXm5mLHjh3o1q0b5s6d22Tb9jz2hBD1ef2z4Pbt2xCLxVi8\neDHs7OzA5/NlHiMhb91obX2RV0pKCjIyMvD06VOIxWI8fvwYISEhMDExUWm/r2tu3HJycnDnzh0A\nLycm33zzDd566y3ptjojR45Ely5dEBoaimvXrmHjxo1qPY6mvP5eAVRbD65duwZ9fX0AzY9vHRMT\nE8ycORMxMTHYvn075s+fL/N63eqxN2/eVEnOhBBZNClsI7p27Ypp06bhl19+wb59+1BSUoLk5GS5\nn+3E5/OxYsUKHDlyBIcOHUJJSQlu376NRYsWwdLSEl5eXjLtJRIJCgsLUVNTg+TkZCxbtgw2NjbS\nxzAoEs/BwQEFBQWIiYmBWCzG06dP8ejRo3o5mpqaIicnBw8fPkRpaancxevOnTvYunUrwsLCoKOj\nI13quu7n1W93T548qdAS4YwxvHjxAhKJBIwxPH36FBERERg+fDi0tLQQExPT7D2F7XnsCSGq09xn\ngY2NDQDg7NmzqKysRFpamsw9xfLWjdbWF3ktXboUNjY2ePHihVLjKqq5ccvJycHChQtx9+5dVFdX\n48aNG3j06JHMBOtVkyZNwuzZs7Fp0yZcu3ZNul3RetMazb1XANXUA7FYjLy8PCQkJEgnhc2N76sW\nLVqEqqoqHDt2DBMnTpR5jc/nY86cOTh8+DBCQkJQUlKC2tpaZGVl4cmTJ4oOESGkOYxwoiWrN5WW\nlrJPP/2Ude7cmRkYGDBXV1f21VdfMQCsW7du7NatW2zLli1MIBBIH5Vw8OBB6f4SiYRt27aN9erV\ni+no6DATExM2ZcoUlpqaKtOPl5cX09HRYdbW1kxbW5uJRCL24YcfsoyMDJl28sZ7/vw5GzlyJOPz\n+czW1pb93//9H1u5ciUDwBwcHKRLZl+/fp316NGDCQQC5urqKvNohabcvn2bAWj0Z9u2bdK2J06c\nYIaGhjIrnr3u6NGjrH///kwoFDJdXV3WqVMnBkC60ujbb7/NNmzYwJ4/fy6zX0cce8Zo9baWoNXb\nNIumrj4qz2eBr68vMzU1ZcbGxmzGjBls165dDACzt7dnjx8/lqtuMCZffdm5cyezsLBgAJhQKGST\nJk1iu3fvZkKhkAFgvXr1YhkZGSw0NJSJRCIGgPXo0YPdu3ePMcZYfHw869y5s8zns46ODuvbty+L\njo5WaHyCgoKk/fbs2ZOdP3+ebd68mRkZGTEAzNzcnP30008sPDycmZubMwDMxMSEHT58uNlxO3/+\nPBs2bBgzMTFhWlpazMrKiq1evZrV1NSw6OhoZmJiIu03Pz+flZSUsO7duzMAzMDAgB04cIAxJl+9\nuXz5MnvjjTekdcbCwoJt2rRJoXGV973S0nrw3XffMXt7+ybrLAB25MgRud+Xr3rzzTeZv79/g+NT\nVVXFfH19mY2NDdPW1mZdu3Zl06ZNYykpKU3WXHlR/VIc1a+2ranVR3mMMab8qSZpTmRkJGbOnAlN\nHP6FCxciKioKz58/5zqVDqctjv2MGTMAAFFRURxn0nZo8t9/R6Tq30dL4rfFz4LmhISEIC0tTXrv\nGQBUV1fDz88PISEhKCwslFnIhcinrb9XJkyYgF27dsHW1lbtfVP9UhzVr7atid9fFK0+ShpUt2w0\nUT8ae0II0L4+C3Jzc+Ht7V3v/jBdXV3Y2NhALBZDLBbTpLCF2tJ7RSwWSx9RkZycDD6fz8mEkBAi\ni+4pJBrp7t279e4NbOjH09OT61QJIYQ0QyAQQEdHB/v27UNeXh7EYjFycnKwd+9efPXVV/D09ERO\nTg597ncAvr6+SEtLw7179zBnzhx8/fXXXKdECAFNCslrVq1ahf3796O4uBi2trb45ZdfOMmjT58+\nci2tGx4ezkl+qqApY08039mzZ+Hv7w+JRIIpU6bAxsYGfD4f1tbWmDx5MpKTkxWKp6w4r8fcsWMH\nhg0b1uDrGzZsgJOTE0QiEfT09ODg4IAvvviiwUVIfv75ZwwZMgSGhobo0aMH5syZg9zcXOnrR48e\nxZYtW9rU2ZKmtMfPAiMjI5w+fRr//e9/4ejoCIFAACcnJ+zfvx+bN2/Gjz/+2CE/91urLb5XhEIh\n+vTpg/fffx/r1q3TuOc7EuVbuHChzBc7n3zySb02yq5rr2quHnERr7njbKyuxcTEyIxlly5dWnUs\nMlRzGyNpDt2oS9oLulFfca35+//qq6/YxIkTWUlJCROLxaxz587s/PnzrKysjN2/f5+5u7szIyMj\nlp2dLXdMZcWpc+/ePTZ8+HAGgA0YMKDBNiNGjGC7d+9mz58/ZyUlJSwiIoLp6OiwsWPHyrQLDw9n\nANiWLVtYUVERu3HjBrOzs2MDBw5kYrFY2i4oKIiNGDGCFRYWKpyvJi40QwhRPapfimvpwlmmpqbs\n5MmTLDU1lVVWVsq8roq6VkeeesRFPHmOs6G6JpFIWFZWFktMTGTjx49nnTt3VqjfphaaoTOFhJA2\nr6KiQmnfAHLZR3M2b96M8PBwREZGwtDQEADg4uICV1dXCIVC2NraYtOmTSguLsYPP/ygUGxlxbl1\n6xb8/PywaNEiDBw4sNF2BgYG8PLygqmpKQwNDeHh4YEpU6bg1KlTyMzMlLbbs2cPrKyssHLlShgZ\nGWHgwIFYvnw5bt68KbPMvY+PDwYMGIDx48dr1EPECSGkKR2lfgkEAowdOxaOjo7Q09OTbldlXZO3\nHnEVr7njbKiu8Xg8WFtbw83NDb169Wp1Dq+iSSEhpM3bt28f8vPz23wfTUlPT8eaNWuwfv168Pl8\nAC8fRv3bb7/JtLOzswMAZGRkyB1bWXEAYMCAAYiOjsasWbNkCv/rjh07Bi0tLZltdZfBlJeXS7dl\nZmbC0tJS5uHX3bt3B4B6z1hbt24dbt68iaCgIIVyJoQQrnSE+tUYVdY1QP56xEU8eY9TnXWNJoWE\nELVjjCEwMBB9+/aFnp4eTExM8OGHH+Lu3bvSNt7e3tDV1YWFhYV025IlS6Cvrw8ej4dnz54BAJYt\nW4YVK1YgIyMDPB4PDg4OCA4OBp/Ph5mZGRYuXAhLS0vw+XwMGzZM5uxSa/oAgFOnTqnt4dTBwcFg\njGHSpElNtquoqAAAiESiVvWnrDiKyM7OhkAgkFmJ0M7Ort4/M3X3E9YV0DomJiYYMWIEgoKCaLl0\nQohKUP1SHnXXNU3X0HGqs67RpJAQonbr1q2Dv78/Vq9ejfz8fCQmJiIzMxNubm7Iy8sD8LJYeHh4\nyOy3e/durF+/XmZbUFAQJk6cCHt7ezDGkJ6eDm9vb8yePRvl5eXw8fHBw4cPcf36ddTU1MDd3V16\neWJr+gD+twy8RCJR3uA04vjx4+jduzeEQmGT5E+pWAAAIABJREFU7a5cuQIAcHV1bVV/yoojr/Ly\ncsTHx2P+/PnQ1dWVbl+1ahVyc3Oxc+dOlJaWIiUlBUFBQfjggw8wdOjQenHefPNNZGdn49atW2rJ\nmxDSsVD9Uh511zVN19hxqquu0aSQEKJWFRUVCAwMxNSpU/HJJ5/AyMgIzs7O+P777/Hs2TOEhoYq\nrS9tbW3pt7lOTk4ICQlBaWkp9u/fr5T4EyZMQElJCdasWaOUeI0pKyvDgwcPYG9v32ibvLw8hIeH\nw8fHBy4uLs1+86rqOIoKCAiApaUlNm7cKLN9xIgR8PX1hbe3N0QiEfr164fS0lLs3bu3wTh191jc\nvn1b5TkTQjoWql/Ko866pumaO0511TV6eD0hRK1SUlLw4sULDB48WGb7kCFDoKurK3N5jLINHjwY\nQqFQ5jKftiA/Px+MsSa/TXVxcUFZWRk8PDywceNG6cOhFaWsOIo4cuQIIiMjcfr0aelCA3VWr16N\nvXv34ty5c3jnnXeQn58PPz8/uLi44OLFi9L7C+vUjVHdN/aEEKIsVL+UR511TdM1d5zqqms0KSSE\nqFVRURGAl6tPvs7Y2BilpaUq7V9PTw9Pnz5VaR/KVllZCQBN3thuZmaGffv24Y033mhVX8qKI6/w\n8HAEBgYiISEBVlZWMq89efIEW7Zsgb+/P0aNGgUAsLW1RVhYGExMTLBt2zYEBwfL7CMQCAD8b8wI\nIURZqH4pjzrrmqZr7jjVVddoUkgIUStjY2MAaLB4FhUVoVu3birrWywWq7wPVagrCE09nL1r167S\nsW0NZcWRx86dOxEXF4f4+PgG/8lKS0tDbW1tvcmiSCSCqakpUlJS6u1TXV0N4H9jRgghykL1S3nU\nWdc0XXPHqa66RpNCQoha9evXDwYGBrh69arM9qSkJFRXV2PQoEHSbdra2hCLxUrrOyEhAYwxmQVK\nlN2HKpiZmYHH46G4uLjRNq8vbd1SyorTFMYY/Pz8UFhYiJiYGGhrN1yK6v75efLkicz20tJSFBQU\n1Lt0FIB0jMzNzZWcNSGko6P6pTzqrGuarrnjVFddo4VmCCFqxefzsWLFChw5cgSHDh1CSUkJbt++\njUWLFsHS0hJeXl7Stg4ODigoKEBMTAzEYjGePn1a79l0AGBqaoqcnBw8fPgQpaWl0iIpkUhQWFiI\nmpoaJCcnY9myZbCxscHs2bOV0sfJkyfVsqS3UCiEnZ0dsrKyGnw9PT0d5ubmmDlzZr3XPD09YW5u\njuvXrzfbj7LiNOfOnTvYunUrwsLCoKOjAx6PJ/Ozfft2AC8vFR05ciTCwsKQmJiIiooKZGZmSt8j\n8+bNqxe7boycnZ1bnSchhLyK6pfyqKuuyYPLeE0dZx111TWaFBJC1G7t2rUICAjAhg0b0KVLF4wY\nMQI9e/ZEQkIC9PX1pe0WL16MkSNH4qOPPkLv3r3x9ddfSy+fcHFxkS7NvWjRIpiZmcHJyQnjx49H\nQUEBgJfX3zs7O0MgEMDNzQ2Ojo74/fffZe5haG0f6jJhwgSkpKRIn2P0qqaeXVRdXY38/HzExsY2\n24cy4ly+fBmurq6wsrJCUlISbt26BUtLSwwfPhyJiYnN9vMqHo+HqKgoeHp6Yt68eTAxMYGTkxMe\nP36M6OhouLm51dvnzz//hLW1Nfr37y9XH4QQogiqX8qj6romTz3iMl5zx1lHbXWNEU5EREQwGn7S\nHkyfPp1Nnz6d6zTq8fLyYqamplyn0aCW/P2npaUxbW1tdvDgQYX2q62tZW5ubmzfvn0K7aeqOKr0\n7Nkzxufz2fbt2xXaT9Wfx/R5T4hmovqluJZ8nnl5eTFra+t627mua20hXlN1zcfHh3Xu3FmheE38\n/iLpTCEhpN1q6gb2tsbBwQEbNmzAhg0b8OLFC7n2qa2tRUxMDEpLS+Hp6dnivpUVR9XWrVuHgQMH\nwtvbm+tUCCGkVdpT/QJePuMxLi4OaWlp0oVTuKxrbSXe63WNMYacnBxcuHAB6enprY7/KpoUEkJI\nG+Hv748ZM2bA09OzyZvz6yQkJCA6OhonT55s8llQ6oqjSoGBgbh58yZOnDjRbp9lRQghbVVBQQHG\njh0LR0dHzJ07V7qdq7rWFuI1VNdiY2NhbW0NNzc3HD9+vNX5voomhYSQdmfVqlXYv38/iouLYWtr\ni19++YXrlJRm06ZN8Pb2xjfffNNs29GjR+Onn36ChYVFq/pUVhxViY2NRVVVFRISEmBiYsJ1OoQQ\n0mLtsX59//33YIxJfw4dOiTzOhd1TdPjNVbXPvzwQ5mxfPbsWWtTlqJHUhBC2p2AgAAEBARwnYbK\njBkzBmPGjOE6DY0xefJkTJ48mes0CCGk1dp7/WoM1TVZXNQ1OlNICCGEEEIIIR0YTQoJIYQQQggh\npAOjSSEhhBBCCCGEdGA0KSSEEEIIIYSQDowWmuHYjBkzuE6BkFa5fPkyAM16L5eVlUFfX5/rNBqV\nlZUFQLPGrCOr+32oGv2+CQA8f/4cVVVVsLKy4jqVDk8T65emo/rVtjVV73iMMabGXMj/d+nSJQQG\nBnKdBiHtTkVFBU6dOgUHBwf069cPPB6P65RIGxEVFaWSuPR5T6qrq/Ho0SM8ePAAJSUlsLCwgKur\nK9dptTkFBQW4cOEC3N3dIRAIuE6HkDargXoXRZNCQki7Ex4ejrlz5+K9995DeHg4RCIR1ykRQjqg\na9euITQ0FIcOHQJjDH/729+wYMECvP/++1yn1ibFx8dj9OjRePr0Kbp06cJ1OoS0J1F0TyEhpN3x\n9PTEuXPncP36dbi6uuLRo0dcp0QI6SAKCwsRGhoKZ2dnDB48GNeuXcOOHTuQl5eHyMhImhC2QlVV\nFQCAz+dznAkh7Q9NCgkh7ZKLiwuuXr0KbW1tDB48GOfPn+c6JUJIOyWRSHD27Fl4eHjAwsICK1eu\nxLBhw3D9+nVcvXoVCxYsgKGhIddptnl1k0I9PT2OMyGk/aFJISGk3erWrRsSExMxbNgwuLu74+DB\ng1ynRAhpR548eYItW7agV69ecHd3x/3797Fz507k5ORgz549ePPNN7lOsV2pqqpCp06doKOjw3Uq\nhLQ7tPooIaRdMzAwwJEjR7B69Wr885//REpKCgICAtCpE30nRghRnEQiQXx8PEJDQxETEwN9fX14\neHhgyZIl6N+/P9fptWtVVVV0lpAQFaFJISGk3dPS0sLmzZvh4OCAJUuWICMjAz/++COEQiHXqRFC\n2ojs7GwcOnQI3333HR49eoRBgwZh165d+OSTT+izRE1oUkiI6tCkkBDSYXz66afo06cPpk6diuHD\nh+Po0aPo3r0712kRQjRUbW0tfv/9d4SGhuLXX39F586dMXPmTCxYsABvvPEG1+l1OJWVlbTIDCEq\nQtdPEUI6FFdXV1y6dAlVVVUYOnQorl69ynVKhBANk56ejnXr1sHW1hYffPABCgsL8fPPPyMzMxPf\nfvstTQg5QmcKCVEdmhQSQjoce3t7JCUl4c0334SbmxsOHz7MdUqEEI5VVVUhKioK7u7ucHR0RGho\nKD7++GOkp6fjzJkzmDFjBi1wwjGaFBKiOnT5KCGkQzI0NERsbCw+++wzzJo1C6mpqVi7di14PB7X\nqRFC1Cg1NRX79+/Hv//9bzx//hyjRo1CREQEpkyZAm1t+jdJk9CkkBDVoU87QkiHpaWlheDgYPTr\n1w9Lly7F3bt3sX//fggEAq5TI4SoUGVlJX777TeEhobi3LlzsLKywty5c7Fo0SL06NGD6/RII6qq\nquieQkJUhCaFhJAOb8GCBbC1tYWHhwdGjRqFmJgYmJubc50WIUTJ/vrrL/z444/Yu3cvioqKMHLk\nSERERGDq1KnQ0tLiOj3SjPLycvrSjhAVoXsKCSEEgLu7O65cuYLCwkIMHjwY169f5zolQogSVFRU\nSO8VdHJywpEjR7By5UpkZ2dL7xWkCWHbUFBQAFNTU67TIKRdokkhIYT8f7169cLFixfRq1cvjBgx\nArGxsVynRAhpoWvXrsHLywvm5ub4+9//DhMTE5w5cwapqanw9fWlqwHaoMLCQpiYmHCdBiHtEk0K\nCSHkFaampjh9+jT+/ve/Y8qUKVi3bh3XKRFC5FRSUoLQ0FC89dZbGDx4MBITE7F69WpkZWUhMjIS\n77//Pi0m1YbRpJAQ1aF7Cgkh5DXa2toICQlB7969sXz5cmRmZuK7776Drq4u16kRQhpw7do1hIaG\n4qeffkJtbS0mTpyIrVu3YvTo0TQJbEcKCwthbGzMdRqEtEs0KSSEkEb4+PjA0dERnp6eSE1Nxa+/\n/oquXbtynRYhBEBRUREiIyOxe/duJCcnw8nJCWvWrMH8+fPpvrN2is4UEqI6dPkoIYQ0Ydy4cbhw\n4QKys7Ph4uKCO3fucJ0SIR1a3b2C1tbW+PzzzzFgwACcOXMGKSkp8PX1pQlhO1ZcXEyTQkJUhCaF\nhBDSDGdnZ/z555+wsrLC0KFDcfz4ca5TIqRDycvLw7fffot+/fph8ODBuHbtGnbs2IGcnBwcOHAA\n77//PtcpEhUrKytDVVUVTQoJURGaFBJCiBy6dOmCM2fOYMqUKZg8eTKCg4O5TomQdk0ikeDs2bPw\n8PBA9+7dsXbtWgwfPhw3btzA1atXsWDBAhgYGHCdJlGTwsJCAKBJISEqQvcUEkKInPT09PDDDz/A\nyckJn332GVJSUrBr1y7o6OhwnRoh7caTJ09w4MAB7NmzBw8ePMCgQYOwa9cuzJo1C/r6+lynRzhC\nk0JCVIsmhYQQogAejwdfX184ODjgH//4Bx48eIDIyEhaEY+QVpBIJIiPj0doaCh+/fVXGBgYwMPD\nA0uXLoWzszPX6RENUFRUBIAmhYSoCl0+SgghLTBt2jRcvHgRqampePvtt5Gamsp1SoS0OdnZ2diy\nZQvs7Ozg7u6O+/fvY/fu3cjJycGePXtoQkik6s4U0hdwhKgGTQoJIaSFBgwYgEuXLsHY2BjDhg3D\n77//znVKhGi82tpa6b2CPXr0QFBQEDw9PZGeni69V1AgEHCdJtEwhYWFEAqF0NPT4zoVQtolmhQS\nQkgrWFlZITExEWPHjsWYMWMQEhLCdUqEaKS0tDT4+fnB2toaH3zwAQoLC3H48GE8fvwYmzdvhr29\nPdcpEg1GD64nRLXonkJCCGklPp+PQ4cOoVevXli6dClSU1MRGBgILS0trlMjhFNVVVU4evQoQkND\nce7cOVhaWmL27Nnw8vKCra0t1+mRNuTJkyewtLTkOg1C2i2aFBJCiBLweDysW7cOffv2xZw5c3Dv\n3j2Eh4fDyMiI69QIUbvU1FTs378f//73v/H8+XOMGjUKERERmDJlCrS16V8PoricnByaFBKiQnT5\nKCGEKNHMmTMRHx+PmzdvwtXVFQ8fPuQ6JULUorKyElFRUXB3d0ffvn3x008/Ye7cuXjw4AHOnDmD\nGTNm0ISQtBidKSREtWhSSAghSjZ06FBcvXoVurq6GDJkCBITE7lOiRCVuXPnjvRewU8++QR8Ph8R\nERF4+PAhNm/eDBsbG65TJO1ATk4OrKysuE6DkHaLJoWEEKIC1tbW+OOPPzB8+HCMGTMGBw4c4Dol\nQpSmtLQUBw4cgLu7O9544w0cOXIEX3zxBTIzM/Hbb79hxowZdE8tUSo6U0iIatF1HIQQoiIGBgb4\n9ddfsX79evzzn//EtWvXsGPHDnTqRN/Hkbbp2rVrCA0Nxc8//wyxWIxJkybhzJkzGD16NHg8Htfp\nkXaqoqICRUVFNCkkRIVoUkgIISpUtwCNjY0NFi1ahOzsbPz444/Q19fnOjVC5FJcXIyIiAh8//33\nuHHjBvr06YMvv/wS8+bNQ5cuXbhOj3QAT548AQBYWFhwnAkh7Rd9XU0IIWowd+5cxMfH4/z58xg+\nfDgeP37MdUqENOnatWvw8vKCtbU1fHx84ODggDNnzuCvv/6Cr68vTQiJ2jx69AgA0KNHD44zIaT9\nokkhIYSoyfDhw3Hp0iWIxWIMHToUf/75J9cpESKjqKgIoaGhGDBgAAYPHowLFy5gzZo1yM7ORmRk\nJN5//32uUyQd0KNHjyAQCGBmZsZ1KoS0WzQpJIQQNbKzs8Ply5cxaNAgvPvuu/jpp58abSuRSPD8\n+XM1Zkc6qrqzglZWVli5ciWGDh2K8+fPIyUlBb6+vjA1NeU6RdKBPXz4EDY2NnTfKiEqRJNCQghR\nM0NDQ8TExMDHxwd///vf4efnB8ZYvXZffvkl5s+fz0GGpCPIzc3Fli1b4ODggMGDB+PatWsICgpC\ndnY29uzZA1dXV65TJATAyzOFPXv25DoNQto1WmiGEEI4oKWlhc2bN8POzg5Lly7FgwcP8MMPP0Ag\nEAAADh48iG+++QYAcPbsWbpsjyiFRCJBfHw8QkNDERMTA6FQiJkzZ+KXX37BwIEDuU6PkAY9evQI\nvXr14joNQto1OlNICCEcWrBgAU6cOIHTp09j1KhRyM3NxcWLFzFv3jwALyeP8+fPR1VVFceZEk0h\nkUgU3icnJ0d6VtDd3R3379/Hrl27kJOTgz179tCEkGi0R48e0SIzhKgYTQoJIYRj77//Pi5duoRn\nz55h6NCh+Nvf/ib9x7+2thaZmZnYuXMnx1kSrtXU1GDx4sU4duyYXO0lEgnOnj0LDw8P9OjRA1u2\nbIG7uzuSk5Nx9epVLFiwAEKhUMVZE9I6tbW1yMrKostHCVExmhQSQogG6NOnD86ePQuJRIIXL16g\ntrZW+lptbS2++uorZGdnc5gh4VJJSQnGjh2L7777Dt9//32TbbOysrBlyxbY2trigw8+QE5ODnbv\n3i29V9DZ2VlNWRPSeo8ePUJ1dTUcHBy4ToWQdo3uKSSEEA0gkUiwePFi5ObmQiwW13u9pqYGn3/+\nOQ4fPsxBdoRLmZmZGDNmDDIyMgAAcXFxyMrKQrdu3aRtqqurERcXh4MHD+LIkSPo2rUr/vnPf2L+\n/Pmwt7fnKnVCWi01NRUA4OjoyHEmhLRvdKaQEEI0wOeff464uLgGJ4QAIBaLER4ejoSEBPUmRjiV\nnJyMt99+GxkZGdL3hpaWFvbv3w8ASEtLg5+fH7p3744PP/wQhYWFOHz4MB4/fozNmzfThJC0effu\n3YOZmRmMjY25ToWQdo3HGloHnRBCiNqEhYVhwYIFzbbT0tJCr169cPv2bWhr04Ue7V1cXBymTp2K\n6upq1NTUyLxmamqKfv364fz58+jevTvmzp2LuXPnonv37hxlS4hqLFmyBLdv30ZiYiLXqRDSnkXR\nmUJCCOFYeXm5dLl1XV3dRtvV1tbi3r17CAkJUVdqhCNhYWEYP348Kisr600IAaCgoABisRjHjh3D\n/fv3sXbtWpoQknbp3r17dOkoIWpAk0JCCOGYj48P7t27h6tXr2LhwoUwNjYGj8eDlpZWvbYSiQT+\n/v548uQJB5kSVWOMYe3atViwYAEkEkmjj5/Q0dGBmZkZxo8f3+D7hJD2IjU1lSaFhKgBTQoJIURD\nDBo0CN9++y1yc3MRGxuLyZMnQ0tLq96lomKxGP7+/hxlSVSlsrISM2fOxMaNG5ttW3eWkL4cIO1Z\nRUUFsrOz0bt3b65TIaTdo0khIYRoGD09PUycOBHR0dF4+PAh1q9fDzs7OwAvLy8Vi8U4cOAALl26\nxHGmRFmeP3+O9957D7/++qvcD6fn8Xg4cOCAijMjhDt//fUXJBIJ+vTpw3UqhLR7tNAM4dylS5eQ\nmZnJdRqEaLzU1FQkJCTgP//5D6qqqtCzZ09s3rwZPB6P69RIK2RnZ2PTpk14/vx5g6/zeDx06tQJ\nPB5P+rtmjKG2thZdu3ZFcHAwvQeISgwbNkzm0Sfqtn//fixZsgSlpaV0mTQhqhVFk0LCuRkzZuCX\nX37hOg1CCCGEvCIiIgIeHh6c9b98+XJcuHABV65c4SwHQjqIKFrTnGiE6dOnIyoqius0CGlzioqK\ncPr0acycORP0HZ9ieDwe5//0EqKpNOHsc3JyMvr37891GoR0CHRPISGEtGH0QGdCSHt1+/ZtODs7\nc50GIR0CTQoJIYQQQohGyc3NRX5+Pp0pJERNaFJICCGEEEI0SnJyMgCgX79+HGdCSMdAk0JCCCGE\nEKJRkpOTYWVlha5du3KdCiEdAk0KCSGEEEKIRklKSsKQIUO4ToOQDoMmhYQQQgghRKMkJSXhnXfe\n4ToNQjoMmhQSQggBAJw4cQJGRkb47bffuE5FIy1cuFD6AHkej4dPPvmkXpuzZ8/C398fEokEU6ZM\ngY2NDfh8PqytrTF58mTpfVLyUlac12Pu2LEDw4YNa/D1DRs2wMnJCSKRCHp6enBwcMAXX3yBFy9e\n1Gv7888/Y8iQITA0NESPHj0wZ84c5ObmSl8/evQotmzZgtra2hbn+yplj++rmhsXLuI1d5yNjW9M\nTIzMe7VLly6tOhZ1y83NRWZmJt5++22uUyGkw6BJISGEEACg5xzKwdTUFCdPnkRqair27dsn89ra\ntWsRHByMVatWQSKR4Pz58/j5559RUFCACxcuoKKiAu+++y5ycnLk7k9ZceqkpaXh3XffxfLly1Fe\nXt5gm/j4eCxduhQPHz7Es2fPEBAQgKCgIMyYMUOmXUREBGbNmoUZM2YgKysLsbGxSExMxLhx41BT\nUwMAmDRpEvh8PkaPHo2ioiKF832VKsa3jjzjwkW85o6zsfGdPHkysrKykJiYiPHjx7f6eNQtKSkJ\nPB4PgwYN4joVQjoMmhQSQggBAEyYMAHFxcWYOHEi16mgoqJCaWdslEkgEGDs2LFwdHSEnp6edPvm\nzZsRHh6OyMhIGBoaAgBcXFzg6uoKoVAIW1tbbNq0CcXFxfjhhx8U6lNZcW7dugU/Pz8sWrQIAwcO\nbLSdgYEBvLy8YGpqCkNDQ3h4eGDKlCk4deoUMjMzpe327NkDKysrrFy5EkZGRhg4cCCWL1+Omzdv\nIikpSdrOx8cHAwYMwPjx46WTRUWpcnzlHReu4jV3nA2NL4/Hg7W1Ndzc3NCrV69W56BuV65cQd++\nfek5rISoEU0KCSGEaJx9+/YhPz+f6zTkkp6ejjVr1mD9+vXg8/kAAG1t7XqX4drZ2QEAMjIy5I6t\nrDgAMGDAAERHR2PWrFkyE9rXHTt2DFpaWjLb6i4/fPWsV2ZmJiwtLcHj8aTbunfvDgB49OiRzP7r\n1q3DzZs3ERQUpFDOgGrHF5B/XLiIJ+9xtmZ8NVFSUhJdOkqImtGkkBBCCC5cuAAbGxvweDzs2rUL\nABASEgJ9fX0IhULExsZi3LhxEIlE6NatGw4fPizdNzg4GHw+H2ZmZli4cCEsLS3B5/MxbNgwmTNG\n3t7e0NXVhYWFhXTbkiVLoK+vDx6Ph2fPngEAli1bhhUrViAjIwM8Hg8ODg4AgFOnTkEkEmHTpk3q\nGBK5BQcHgzGGSZMmNdmuoqICACASiVrVn7LiKCI7OxsCgQC2trbSbXZ2dvUm7nX3E9ZNXOqYmJhg\nxIgRCAoKUvgyZXWPr6Zr6DhbM76aRiKR4OrVq7TyKCFqRpNCQgghcHV1xcWLF2W2LV68GJ999hkq\nKipgaGiIiIgIZGRkwM7ODvPnz4dYLAbwcrI3e/ZslJeXw8fHBw8fPsT169dRU1MDd3d36SWHwcHB\n8PDwkOlj9+7dWL9+vcy2oKAgTJw4Efb29mCMIT09HQCki2lIJBKVjEFLHT9+HL1794ZQKGyy3ZUr\nVwC8HOvWUFYceZWXlyM+Ph7z58+Hrq6udPuqVauQm5uLnTt3orS0FCkpKQgKCsIHH3yAoUOH1ovz\n5ptvIjs7G7du3VKof3WPr6Zr7DhbOr6a5vbt2yguLsbw4cO5ToWQDoUmhYQQQpo1bNgwiEQidO3a\nFZ6enigrK8Pjx49l2mhra6Nv377Q09ODk5MTQkJCUFpaiv379yslhwkTJqCkpARr1qxRSjxlKCsr\nw4MHD2Bvb99om7y8PISHh8PHxwcuLi7NnvFSdRxFBQQEwNLSEhs3bpTZPmLECPj6+sLb2xsikQj9\n+vVDaWkp9u7d22Ccunvbbt++LXff6hxfTdfccbZkfDVRQkICTE1N4ezszHUqhHQo2lwnQAghpG2p\nO1tUd6awMYMHD4ZQKMTdu3fVkRYn8vPzwRhr8iyWi4sLysrK4OHhgY0bN0JHR6dFfSkrjiKOHDmC\nyMhInD59WrrAS53Vq1dj7969OHfuHN555x3k5+fDz88PLi4uuHjxovT+wjp1Y5SXlyd3/+ocX03X\n3HG2ZHw10R9//IF3330XnTrReQtC1IkmhYQQQlRGT08PT58+5ToNlamsrASAJhcUMTMzw759+/DG\nG2+0qi9lxZFXeHg4AgMDkZCQACsrK5nXnjx5gi1btsDf3x+jRo0CANja2iIsLAwmJibYtm0bgoOD\nZfYRCAQA/jdm8lDn+Gq65o6zJeOraRhjOH/+PFavXs11KoR0ODQpJIQQohJisRhFRUXo1q0b16mo\nTN0/4k09nL1r165KWVpfWXHksXPnTsTFxSE+Ph4GBgb1Xk9LS0NtbW29yaJIJIKpqSlSUlLq7VNd\nXQ3gf2MmD3WOr6Zr7jhbMr6aJjk5Gc+ePcOIESO4ToWQDocmhYQQQlQiISEBjDGZRUe0tbWbvey0\nLTEzMwOPx0NxcXGjbV5/pEBLKStOUxhj8PPzQ2FhIWJiYqCt3fC/CXUT/SdPnshsLy0tRUFBQb1L\nRwFIx8jc3FzufNQ5vpquueNsyfhqmj/++APGxsbo378/16kQ0uHQBduEEEKUQiKRoLCwEDU1NUhO\nTsayZctgY2OD2bNnS9s4ODigoKAAMTExEIvFePr0ab1n2gGAqakpcnJy8PDhQ5SWlkIsFuPkyZMa\n90gKoVAIOzs7ZGVlNfh6eno6zM3NMXPmzHqveXp6wtzcHNevX2+2H2XFac6dO3ewdetWhIWFQUdH\nBzweT+Zn+/btAF5eKjpy5EiEhYUhMTEcu62OAAAgAElEQVQRFRUVyMzMhJeXFwBg3rx59WLXjVHd\nAiLy5K2u8ZUHl/GaOs46r49vW1R3P+Hrz8kkhKgeTQoJIYRg165d0ueC+fr6YvLkyQgJCcGOHTsA\nAP3798f9+/cRFhaGFStWAADGjh2LtLQ0aYzKyko4OztDIBDAzc0Njo6O+P3332XuB1u8eDFGjhyJ\njz76CL1798bXX38tvdzNxcVF+viKRYsWwczMDE5OThg/fjwKCgrUMg4tMWHCBKSkpEifH/eqpp4Z\nV11djfz8fMTGxjbbhzLiXL58Ga6urrCyskJSUhJu3boFS0tLDB8+HImJic328yoej4eoqCh4enpi\n3rx5MDExgZOTEx4/fozo6Gi4ubnV2+fPP/+EtbW19CyQvHmrenzlGRcu4zV3nHVeH9+2pra2FgkJ\nCXjvvfe4ToWQjokRwrHp06ez6dOnc50GIW1WREQE4/rj3MvLi5mamnKag6IAsIiICLnbe3l5MWtr\n63rb09LSmLa2Njt48KBC/dfW1jI3Nze2b98+hfZTVRxVevbsGePz+Wz79u3SbfLmzfX4toV4DY1v\nHR8fH9a5c2eFYyr699FaFy5cYADYX3/9pbY+CSFSkXSmkBBCiFI0tRhIe1FRUYG4uDikpaVJF/Zw\ncHDAhg0bsGHDBrx48UKuOLW1tYiJiUFpaSk8PT1bnI+y4qjaunXrMHDgQHh7ewNQLG8ux7etxHt9\nfBljyMnJwYULF5Cent7q+Opw4sQJ2Nraok+fPlynQkiHRJNCQlTg008/haGhIXg8Hm7evKmWPrdv\n3y5dlOH7779XS5/KduHCBQwfPhxCoRCWlpbw9fVFVVWVwnGio6NhZ2cnvQ+quYedBwYGgsfjoVOn\nTujTp4/MJV4t0ZLff3v4/XUEBQUFGDt2LBwdHTF37lzpdn9/f8yYMQOenp5NLopSJyEhAdHR0Th5\n8mSTz+BTVxxVCgwMxM2bN3HixAnps/UUzZur8W0L8Roa39jYWFhbW8PNzQ3Hjx9vdb7qcOLECUyY\nMIHrNAjpuLg+V0lIe7189PDhwwwAu3Hjhtr6TEtLYwDYd999p7Y+leW///0vEwgEbM2aNezFixfs\n4sWLrEuXLmzOnDktjmlvb88AMAsLC1ZdXd1gm5qaGtajRw8GgI0ePbrFfb2uJb//lv7+uL581N/f\nn+nq6jIArGfPniwqKoqzXBQBFVweFxcXx3x9fZUasy2LiYlhAQEBrKamRinxaHxlKXt8X6WKv4/G\n5OTkMB6Px06cOKGW/ggh9dDlo4QQzfD111/DwsIC69evh76+PlxcXODr64sffvgBd+/ebXHcQYMG\nITc3FzExMQ2+Hh0dDWtr6xbHJ0BAQACqqqrAGMODBw8wffp0rlPizJgxY7B582au09AYkydPhr+/\nv9JWk6TxlaXs8eXK8ePHwefz6fmEhHCIJoWEqAiPx+M6hTajpqYGx48fx4gRI2TGbdy4cWCMybU6\nX2MWL14MAPjuu+8afD0wMFC6mqYy0e+fEELkc/LkSYwaNUpjL4EmpCOgSSFpk2pra/HVV1/BxsYG\nAoEA/fv3R0REBAAgJCQE+vr6EAqFiI2Nxbhx4yASidCtWzccPny4XqyDBw9i8ODB4PP50NfXR8+e\nPfH1118DeHmzfmBgIPr27Qs9PT2YmJjgww8/rHfmijGGbdu2oXfv3tDT04ORkRFWrlypUN5bt26F\nUCiEoaEh8vPzsWLFClhbWyM1NbVVY3X+/Hk4OTnByMgIfD4fzs7OiIuLA/Dy3re6++7s7e1x48YN\nAMCcOXMgFAphZGSEo0ePqjz3+/fv48WLF7CxsZHZbm9vDwBITk6Wbjt16pRCz6obNWoU+vbti99/\n/71ePv/5z39QXl6OMWPGNLivOn//hBDSEVVVVeHs2bMYN24c16kQ0qHRpJC0SX5+fti6dSt27NiB\nJ0+eYOLEifj4449x9epVLF68GJ999hkqKipgaGiIiIgIZGRkwM7ODvPnz4dYLJbGCQoKwj/+8Q9M\nnz4dOTk5yMrKwqpVq6STh3Xr1sHf3x+rV69Gfn4+EhMTkZmZCTc3N+Tl5UnjrFmzBr6+vvDy8kJe\nXh5yc3Ph5+enUN5ffPEFli9fjhcvXiAgIAC2trYYOnSo3M8Na0xeXh5mzpyJhw8fIicnBwYGBpg1\naxYAYO/evZg2bRq0tLRw/vx5vPnmmwCA/fv3Y8qUKTh06BAmTZqk8txzc3MBAIaGhjLb+Xw+BAKB\nzFjXrXApkUjkHoOFCxcCQL0FXP71r39h+fLlje6nzt8/IYR0RHFxcSgtLZXWGkIIR7i7n5GQlxRd\naKaiooIJhULm6ekp3VZeXs709PTY4sWLGWOMrV69mgFgFRUV0ja7d+9mAFh6ejpjjLHq6mpmbGzM\nRo4cKRO/pqaGBQUFsfLycmZgYCDTD2OMXblyhQFgGzZskPYtFAqZu7u7TLvXFxppad6KkGehkoCA\nAAaA5efnM8YYO3v2LAPANm7cKG1TXFzMevXqJV284P+1d6dhUR3pHsD/rQjNqhBFQERB1Mi4JhiF\niHtM1HFBZTE6MxrjICQPGLlRQY1KlMQlwMUljrhkcZRFCWgimjGK6KhE44gOjgaICrghQaQRRJa6\nH7z02LJIQ8Oh4f97nv6Qc6rfersObXg5daoaO/cff/xRABAhISFVzpmYmAhnZ2e1YwrxbKGZGzdu\niPz8fGFoaChMTU1FUVGREEKIjIwMYW1tLUpKSoRCoaiy0IwU119bF5rRVmjifdiItElTfT9mz54t\nhg0b1uj9EFGtonWavAolaqDr16+jqKgIffv2VR7T19eHhYVFrQuS6OrqAoDyTuHly5eRn5+Pt99+\nW6Vd27Zt4efnhwsXLqCwsBCOjo4q5wcPHgxdXV0kJycDANLT01FUVIQxY8Y0St6aVrlkeeUdt9Gj\nR6NXr17YtWsXAgMDIZPJEBkZCU9PT+XiBY2du1wuB/Ds2cIXPX36FPr6+g2K3759e7z77ruIiIhA\nZGQk5s6di9DQUPj4+EBXV1e539zzUlNTte76u7m5aSROaxIaGoqYmBip0yBqlUpKSnDo0CEEBQVJ\nnQpRq8fpo6R1Hj9+DABYvny58nk4mUyGW7duoaioqM5xCgoKAAAdOnSo9nx+fj4AwMjIqMq5Dh06\nQKFQAACys7MBAJ06dWqSvNX1ww8/YOTIkejUqRP09PSwePFilfMymQwLFizAb7/9hp9++gkA8M03\n32DevHlNlruFhQWA/16TSkVFRXjy5AksLS0b3EflgjPbtm1Dfn4+YmJilNNKq9NSrj8RUXN15MgR\nKBQKTJ8+XepUiFo93ikkrVP5y3doaCgWLlxY7zhWVlYAgNzc3GrPVxaLlb/8Py8/Px/W1tYA/nuX\n62WbrGsqb3VkZmbC1dUV06ZNw65du2BlZYVNmzZVKQznzJmDwMBA7NixA127doWJiQm6devWZLnb\n2trC2NgYt27dUjmenp4OAOjfv3+D+xg4cCCGDh2Kc+fOwcvLC25ubjA1Na2xvTZef97xUo9MJsNH\nH30Ed3d3qVMhanaaYgXlmJgYODs7c1sgomaAdwpJ63Tt2hVyuRyXLl1qUJzu3bvDzMwMP/74Y7Xn\n+/btCyMjoyqLgCQnJ+Pp06d4/fXXle3atGmDkydPNkne6rhy5QpKS0vh4+MDOzs7yOXyav9Hb2pq\nCg8PD8TFxWHjxo2YP3++yvnGzl1HRwcTJkxAUlKSygIyCQkJkMlkGluAoPJu4f79+/HRRx/V2rYl\nXH8ioubqyZMnOHToEKe9EzUTLApJ68jlcsydOxf79u3D1q1bUVBQgPLycmRnZ+Pu3bt1jqOnp4fA\nwEAkJSXB19cXt2/fRkVFBRQKBa5evQq5XA5/f3/ExsZiz549KCgowJUrV+Dt7Q1LS0t4eXkBeHYH\naPr06di/fz927tyJgoICXL58Gdu3b2+UvNVRucXDsWPH8OTJE6SlpSmfhXuRt7c3SkpK8P3332PS\npElNnvuKFStw//59rFy5Eo8fP8bZs2exYcMGzJkzB71791a2S0hIUGtLiue5u7ujY8eOcHV1hZ2d\nXa1tW8L1JyJqro4cOYLCwkJOHSVqLqRe6oZI3dVHhRCipKRELFmyRNjY2AgdHR3RqVMnMX36dJGa\nmiq2bNkiDAwMBADRs2dPkZGRIbZv3y5MTEwEANGtWzfx66+/KmNt3rxZ9OvXT8jlciGXy8WgQYPE\nli1bhBBCVFRUiA0bNoiePXuKdu3aCVNTU+Hq6iquX7+uko9CoRDvv/++eOWVV4SRkZEYNmyY+OST\nTwQAYW1tLVJSUl6a97p164S+vr4AILp27Sq+/fZbtcbkiy++EJ07dxYAhKGhoZg2bZoQQoglS5YI\nMzMz0aFDB+Hm5iY2b94sAIgePXqIzMxMlRiDBg0SAQEBao95Q3OvdPLkSfHGG28IPT09YWlpKT7+\n+GPx5MkTlTaHDx8WxsbGKqulvig2Nlb06NFDABAdO3YUH374ofLc4sWLxZkzZ5T/vXz5cmFhYSEA\niDZt2ggHBwdx6tQpIUTTXv+arl9dcPXR+gFXHyWqUWN/P1xdXcXIkSMbLT4RqSVaJkQDN0EjaqDK\nqSN8Hkp6EydOxObNm2Frayt1KqSG6OhoeHh4NHhPy9ZGJpMhKiqKzxQSVaMxvx/3799H165dsWvX\nLsyePVvj8YlIbTGcPkrUilVuzwE826JDLpezICQiokb19ddfw8DAANOmTZM6FSL6fywKiZqxa9eu\nqWxfUNPL09OzXvGXLFmCtLQ0/Prrr5g7dy4+/fRTrcmdiBru2LFjCAgIQEVFBVxdXWFjYwO5XI4u\nXbpgypQpuHz5slrxNBXnxZihoaFwdnau9nxQUBAcHBxgYmICPT092NvbY/HixSgsLKzSdu/evRg8\neDCMjY3RrVs3zJ07F/fu3VOeP3jwINatW6fcx5Uax1dffYVZs2bBwMBA6lSI6P+xKCRqxl599VUI\nIV76ioyMrFd8AwMDvPrqqxg7dixWrVoFBwcHrcmdiBpm5cqVCA8PR2BgICoqKnDq1Cns3bsXeXl5\nOH36NIqLizF8+HDcuXOnzjE1FadSWloahg8fjkWLFtW4n+fx48fx4Ycf4ubNm8jNzUVwcDDCwsKq\nrGoZFRWFWbNmwc3NDdnZ2YiPj0dSUhLGjx+PsrIyAMDkyZMhl8sxZswY5V6lpFmnT5/Gf/7zH5W9\ncIlIeiwKiVqxNWvWoLy8HJmZmVVWHCVSR3FxcY13crSpj9bi888/R2RkJKKjo2FsbAwAcHJywrBh\nw2BgYABbW1usXbsWjx49wldffaVWbE3FSUlJwdKlS+Ht7Y2BAwfW2M7IyAheXl4wMzODsbEx3N3d\n4erqiiNHjiArK0vZ7m9/+xusrKzw8ccfo3379hg4cCAWLVqES5cuqazK7OfnhwEDBmDChAnKYpE0\nZ+fOnejfvz9ee+01qVMhouewKCQiogbbuXMncnJytL6P1iA9PR0rVqzA6tWrIZfLATzbK/TQoUMq\n7Sq3bcnIyKhzbE3FAYABAwbgwIEDmDVrFvT09Gps9/3336Nt27Yqxzp27AgAKncXs7KyYGlpqbJX\na9euXQEAt27dUnn/qlWrcOnSJYSFhamVM9WuoKAAMTExVfbCJSLpsSgkImqFhBAICQlBnz59oKen\nB1NTU0ydOhXXrl1TtvH19YWuri4sLCyUxz744AMYGhpCJpMhNzcXALBw4UL4+/sjIyMDMpkM9vb2\nCA8Ph1wuh7m5ORYsWABLS0vI5XI4Ozur3JVpSB/As73O6rtvZWsVHh4OIQQmT55ca7vi4mIAgImJ\nSYP601Qcddy+fRv6+voqC2fZ2dlV+aNC5fOEL+5bampqihEjRiAsLIyr+mrQ7t27AQCzZs2SOBMi\nehGLQiKiVmjVqlUICAjAsmXLkJOTg6SkJGRlZcHFxQX3798H8Kx4eHE5+i1btmD16tUqx8LCwjBp\n0iT06NEDQgikp6fD19cXc+bMQVFREfz8/HDz5k1cvHgRZWVleOutt5TT+hrSBwDlgiAVFRWaG5wW\n7ocffkDv3r1fusjHzz//DAAYNmxYg/rTVJy6KioqwvHjxzF//nzo6uoqjwcGBuLevXvYtGkTFAoF\nUlNTERYWhrfffhtDhw6tEmfQoEG4ffs2UlJSmiTvlq68vBybNm3C3LlzYWpqKnU6RPQCFoVERK1M\ncXExQkJCMG3aNMyePRvt27dHv379sG3bNuTm5mL79u0a60tHR0d5N9LBwQFbt26FQqFQ3jFoqIkT\nJ6KgoAArVqzQSLyW7vHjx7hx4wZ69OhRY5v79+8jMjISfn5+cHJyeukdxcaOo67g4GBYWlpizZo1\nKsdHjBiBJUuWwNfXFyYmJujbty8UCgV27NhRbZyePXsCAK5cudLoObcGsbGxuHHjBvz8/KROhYiq\nwaKQiKiVSU1NRWFhIRwdHVWODx48GLq6uirTOzXN0dERBgYGKtNUqenk5ORACFHrXUInJyf4+flh\n6tSpSEhIQLt27erVl6biqCM2NhbR0dE4evSocgGdSsuWLcP27dvx008/obCwEL/99hucnZ3h5OSk\nsiBNpcoxqrxzTg0TGhqKKVOmKKd+E1HzoiN1AkRE1LQql9o3MjKqcq5Dhw5QKBSN2r+enh4ePHjQ\nqH1Q9Z48eQIAtS7cYm5ujp07d+IPf/hDg/rSVJy6ioyMREhICBITE2FlZaVy7u7du1i3bh0CAgIw\nevRoAICtrS0iIiJgamqKDRs2IDw8XOU9+vr6AP47ZlR/Z86cwdmzZ3Hq1CmpUyGiGrAoJCJqZTp0\n6AAA1RZ/+fn5sLa2brS+S0tLG70PqllloVPb5uydOnVS/ow0hKbi1MWmTZtw9OhRHD9+vNo/dqSl\npaG8vLxKsWhiYgIzMzOkpqZWec/Tp08B/HfMqP5CQ0Ph6OjYZM+VEpH6WBQSEbUyffv2hZGRES5c\nuKByPDk5GU+fPsXrr7+uPKajo4PS0lKN9Z2YmAghhMrCHprug2pmbm4OmUyGR48e1djmxS0l6ktT\ncWojhMDSpUvx8OFDxMXFQUen+l9rKv8IcffuXZXjCoUCeXl5yq0pnlc5Rp07d9Zw1q3LjRs38N13\n32HPnj1Sp0JEteAzhURErYxcLoe/vz9iY2OxZ88eFBQU4MqVK/D29oalpSW8vLyUbe3t7ZGXl4e4\nuDiUlpbiwYMHVfZ0AwAzMzPcuXMHN2/ehEKhUBZ5FRUVePjwIcrKynD58mUsXLgQNjY2mDNnjkb6\nSEhI4JYUajAwMICdnR2ys7OrPZ+eno7OnTvDw8OjyjlPT0907twZFy9efGk/morzMlevXsX69esR\nERGBdu3aQSaTqbw2btwI4NlU0VGjRiEiIgJJSUkoLi5GVlaW8md93rx5VWJXjlG/fv0anGdrFhwc\nDBsbG8yYMUPqVIioFiwKiYhaoZUrVyI4OBhBQUHo2LEjRowYge7duyMxMRGGhobKdj4+Phg1ahRm\nzpyJ3r1749NPP1VOp3t+gQ5vb2+Ym5vDwcEBEyZMQF5eHoBnz2P169cP+vr6cHFxQa9evXDixAmV\nZ9oa2gepZ+LEiUhNTVXuH/i82vbke/r0KXJychAfH//SPjQR59y5cxg2bBisrKyQnJyMlJQUWFpa\n4s0330RSUtJL+3meTCZDTEwMPD09MW/ePJiamsLBwQGZmZk4cOAAXFxcqrzn/Pnz6NKlC/r371+n\nPqiq9PR0fP311/jkk09qvItLRM2DTHBXVpKYm5sbACAmJkbiTIi0U3R0NDw8PJrdJtsLFixATEwM\nfv/9d6lTqZZMJkNUVFSVfRJbuvT0dPTp0we7d+/G7Nmz6/y+iooKjBw5EnPmzMF7771X7/41Facx\n/f7777C2tsaaNWvg7+8vdTqS0MT3Y/bs2Th//jxSU1NZFBI1bzG8U0hERI2mtgVNSBr29vYICgpC\nUFAQCgsL6/Se8vJyxMXFQaFQwNPTs959aypOY1u1ahUGDhwIX19fqVPRWqmpqdi3bx+CgoJYEBJp\nARaFRERErUxAQADc3Nzg6elZ66IzlRITE3HgwAEkJCTUusdhU8VpTCEhIbh06RIOHz7cJHsrtlQr\nV66Eg4ODcjYQETVvLAqJiEjjAgMDsXv3bjx69Ai2trbYv3+/1CnRC9auXQtfX1989tlnL207ZswY\n/P3vf4eFhUWD+tRUnMYSHx+PkpISJCYmwtTUVOp0tNbFixcRGxuLNWvWoE0b/qpJpA14P5+IiDQu\nODgYwcHBUqdBLzFu3DiMGzdO6jSajSlTpmDKlClSp6H1li9fDkdHR0yePFnqVIiojlgUEhEREZFG\nHDp0CAkJCThx4gRkMpnU6RBRHfGePhERERE1WElJCfz9/TFz5kyMHDlS6nSISA0sComIiIiowdat\nW4e7d+9iw4YNUqdCRGpiUUhEREREDZKZmYn169djxYoV6NKli9TpEJGaWBQSERERUYP4+fnBysoK\nfn5+UqdCRPXAhWaoWdi/fz8fSCdqIH6H1Ofh4QEPDw+p0yDSav/4xz8QFxeHhIQE6OnpSZ0OEdWD\nTAghpE6CWrezZ88iKytL6jSIqAUTQmDVqlW4desWPvjgAwwePFjqlIiaPWdnZ1hbW9fapqCgAAMG\nDICjoyNiYmKaKDMi0rAYFoVERNQqlJSUYPHixdi0aRMWL16MtWvXom3btlKnRaTV3nvvPRw6dAj/\n/ve/0blzZ6nTIaL6ieEzhURE1Cro6enhf//3f/H1118jPDwcb731FnJycqROi0hrHTx4ELt378aX\nX37JgpBIy/FOIRERtToXL17E9OnTUV5ejgMHDnA6KZGacnNz0a9fP4wfPx67du2SOh0iahjeKSQi\notbntddew/nz59G7d28MHz4cu3fvljolIq3i7e0NuVyOsLAwqVMhIg1gUUhERK1Sx44dceTIEfj5\n+WHevHnw8vJCaWmp1GkRNXtfffUVYmNjsXv3bpiYmEidDhFpAKePEhFRqxcZGYn3338fgwYNQnR0\nNCwtLaVOiahZ+ve//42hQ4fC29sbGzZskDodItIMrj5KREQEANeuXYOrqysKCgqwf/9+ODk5SZ0S\nUbOiUCgwZMgQmJqa4sSJE9DV1ZU6JSLSDD5TSEREBACvvvoqkpOT8cYbb2D48OFYt26d1CkRNRtC\nCMydOxcPHz5ETEwMC0KiFoZFIRER0f8zMTFBbGws1qxZg8DAQPzpT39CcXGx1GkRSe6zzz7DwYMH\nER0dDSsrK6nTISIN4/RRIiKiahw+fBizZ89G9+7dERsbi+7du0udEpEkjh8/jnHjxiEkJAS+vr5S\np0NEmsfpo0RERNWZMGECfv75Z5SVlcHR0RH/+Mc/pE6JqMn99ttv8PT0hLu7OwtCohaMRSEREVEN\n7O3tcebMGYwZMwbjx4/HunXrwAk21Fr8/vvvmDBhAmxsbBARESF1OkTUiFgUEhER1cLIyAhRUVHY\nunUrVqxYoVyhlKgle/r0Kdzc3PD48WPExcXB0NBQ6pSIqBGxKCQiIqqDv/71rzh27BjOnTuHIUOG\n4D//+Y/UKRE1CiEE3nvvPfzyyy84fPgwrK2tpU6JiBoZi0IiIqI6Gj58OC5cuID27dtj6NCh+O67\n76ROiUjjFi9ejOjoaMTGxqJfv35Sp0NETYBFIRERkRqsra1x8uRJuLu7Y/r06Vi6dCkqKiqkTotI\nIzZv3owvvvgCX331FcaMGSN1OkTURFgUEhERqUlPTw8RERHYtm0bQkNDMWnSJDx8+FDqtIga5Jtv\nvoGfnx+Cg4Px7rvvSp0OETUh7lNIRETUAL/88gumTZsGXV1dTrcjrXXgwAF4enrC19cXX3zxhdTp\nEFHT4j6FREREDfH666/jwoULsLGxwZAhQ/D1119LnRKRWuLj4zFz5kz4+PiwICRqpVgUEhERNVCn\nTp1w9OhR+Pr6Ys6cOfDy8kJpaanUaRG91D/+8Q94enriT3/6E8LCwqROh4gkwumjREREGrRv3z68\n//77cHR0RHR0NDp37ix1SkTVOn78OP74xz/C3d0du3btQps2vFdA1Epx+igREZEmzZw5E2fOnEF2\ndjYcHR2RnJwsdUpEVRw9ehSTJk3C5MmTsXPnThaERK0c/wUgIiLSsAEDBuD8+fP4wx/+gBEjRiAi\nIkLqlIiUDh06hKlTp2Lq1KnYs2cP2rZtK3VKRCQxFoVERESNwMzMDAkJCVi9ejUWLFiAP//5zygu\nLpY6LWrl9u7di2nTpmHOnDn49ttvoaOjI3VKRNQMsCgkIiJqJDKZDEuWLEF8fDwOHToEFxcX3Lp1\nS+q0qJXatm0b/vSnP8Hf3x9ffvklp4wSkRL/NSAiImpkf/zjH5GcnIwnT57A0dERP/30k9QpUSvz\n2WefwcfHB2vXrsXnn38udTpE1MywKCQiImoCvXr1wrlz5zBy5Ei88847WLdundQpUStQXl4OX19f\nLF++HJs3b8bSpUulTomImiFuSUFERNSEhBBYv349li1bBnd3d0RERMDQ0FDqtKgFevz4Md59910c\nPXoUX331FTw9PaVOiYiapxgWhURERBJITEyEh4cHLCws8N1338HOzk7qlKgFyc3NxZQpU3Dt2jXE\nxcXBxcVF6pSIqPniPoVERERSGDlyJC5cuAA9PT0MGjQI8fHxUqdELUR6ejqcnZ1x//59nDlzhgUh\nEb0Ui0IiIiKJdO3aFUlJSZgxYwZcXV2xdOlSVFRUSJ0WabETJ07gjTfegLm5Oc6dO4fevXtLnRIR\naQEWhURERBKSy+XYuXMntm3bhtDQUEyZMgX5+flSp0VaaNOmTRg3bhzGjRuHY8eOoWPHjlKnRERa\ngkUhERFRM/DXv/4Vx48fxy+//II33ngD//73v6VOibRESUkJ5s2bBz8/P/j7+2Pv3r2Qy+VSp0VE\nWoRFIRERUTPx5ptv4sKFC+jUqSk+fSUAACAASURBVBOcnJwQExMjdUrUzN25cwcjRoxATEwMvvvu\nO3z++efclJ6I1MZ/NYiIiJoRKysrnDx5Eh988AE8PDzg5+eHsrKyGttfv369CbOj5uTMmTNwdHTE\nw4cPkZycjClTpkidEhFpKRaFREREzYyOjg4+//xzfPPNN9ixYwfGjh2LnJycKu3++c9/YtCgQbh8\n+bIEWVJj2rNnDx48eFDj+fDwcIwaNQqDBw/G+fPn0adPnybMjohaGhaFREREzdTs2bNx+vRpZGZm\nwtHREefPn1eeu3v3LqZOnYri4mLMnTuXq5a2ICkpKZg3bx7mz59f5dyjR48wY8YMLFq0CMuXL8d3\n330HExMTCbIkopaERSEREVEzNmjQIJw/fx6vvvoqhg8fjl27dqG0tBQzZszAo0ePAACXLl3Cli1b\nJM6UNOHx48eYPn06ysvLcfDgQezZs0d57uLFi3B0dMSpU6eQkJCAFStW8PlBItIImRBCSJ0EERER\n1a68vBwBAQHYuHEjnJ2dce7cOZSXlyvP6+vr4/r16+jatauEWVJD/eUvf8HevXtRVlYGmUwGAwMD\nXL16FYmJiViwYAGGDBmCvXv3wtLSUupUiajliGFRSEREpEX8/PwQHh5e5Xi7du0wevRoHDlyRIKs\nSBOioqLg6empcqxdu3bo3r07fvvtN6xYsYJ3B4moMbAoJCIi0hYpKSkYMmQISkpKamwTExODGTNm\nNGFWpAnp6ekYMGAAiouL8eKvZm3atIGfnx9CQkIkyo6IWjgWhURERNogLy8PAwcOxN27d2vcoqJN\nmzYwNTVFWloaTE1NmzhDqq+SkhIMHjwY165dQ2lpabVt9PT0cPnyZfTq1auJsyOiViCG8w+IiIia\nufLycri7uyM7O7vWPQsrKirw6NEjLF68uAmzo4b6n//5H1y9erXGghB4dm1nz56t8hwpEZGmsCgk\nIiJq5m7fvo1OnTpBT08Pbdq0gY6OTo1ty8rKsHPnTpw6daoJM6T6io+Px5YtW15a7JWWluL8+fPY\nuHFjE2VGRK0Ji0IiIqJmzsbGBvv27cPDhw8RFxcHd3d3yOVytG3bttoCsU2bNvjLX/6CJ0+eSJAt\n1VVmZib+/Oc/QyaT1dimTZs2aNeuHQCgY8eOyMjI4N1CItI4PlNIRESkhR49eoT4+HhERkbixx9/\nBAAIIZSb2Ldt2xbLly/HqlWrJMySalJWVgYXFxf88ssvVaaN6urq4unTp9DT04OTkxPeeecdjB07\nFq+99lqtBSQRUT1xoRkiIiJtl5ubi/3792PPnj04c+YMdHR0UFpainbt2iElJQV9+vSROkV6wfLl\ny7F27VoAz7adqNyXcMCAAZg4cSLGjh0LJycn6OrqSpwpEbUCLAqJSLuEhITg7NmzUqdB1GwVFxcj\nOzsbt27dQn5+Pjp27IgRI0bwDlMzkpOTg1OnTkEIAQMDA1haWsLc3Bzm5ubKqaJE2mbRokVwcnKS\nOg2qH64+SkTa5ezZszh37pzUaRA1W/r6+ujZsyfGjh2L8ePHw8LCArm5ucrz2dnZ2L9/v4QZaqf9\n+/cjOzu7wXHKy8tx584dvPbaaxg/fjwmTJiAQYMGoUuXLiwISWvt378fWVlZUqdBDVDz8mVERM3U\n0KFDERMTI3UaRFopOjoaHh4e/A6pSSaT4aOPPoK7u7vUqRA1O5yJoP14p5CIiIiIiKgVY1FIRERE\nRETUirEoJCIiIiIiasVYFBIREREREbViLAqJiIiIiIhaMRaFREREpLbDhw+jffv2OHTokNSpNEsL\nFiyATCZTvmbPnl2lzbFjxxAQEICKigq4urrCxsYGcrkcXbp0wZQpU3D58mW1+tRUnBdjhoaGwtnZ\nudrzQUFBcHBwgImJCfT09GBvb4/FixejsLCwStu9e/di8ODBMDY2Rrdu3TB37lzcu3dPef7gwYNY\nt24dysvL653v8zQ9vs972bhIEe9ln7Om8Y2Li1P5We3YsWODPgtpJxaFREREpDYhhNQpNHtmZmZI\nSEjA9evXsXPnTpVzK1euRHh4OAIDA1FRUYFTp05h7969yMvLw+nTp1FcXIzhw4fjzp07de5PU3Eq\npaWlYfjw4Vi0aBGKioqqbXP8+HF8+OGHuHnzJnJzcxEcHIywsDC4ubmptIuKisKsWbPg5uaG7Oxs\nxMfHIykpCePHj0dZWRkAYPLkyZDL5RgzZgzy8/PVzvd5jTG+leoyLlLEe9nnrGl8p0yZguzsbCQl\nJWHChAkN/jykpQQRkRaZMWOGmDFjhtRpEGmtqKgo0dL+919UVCScnJwatQ8AIioqqs7tvby8RJcu\nXao999lnn4levXqJ4uJiIYQQpaWl4o9//KNKm59//lkAEGvXrq1zn5qKI4QQly5dEtOmTRN79uwR\nAwcOFAMGDKi23cSJE0VZWZnKMXd3dwFAZGZmKo+NGjVKWFlZiYqKCuWxzZs3CwDi9OnTKu/39fUV\nTk5OorS0VK2cKzXW+ApR93GRIl5dP2dt4+vn5ydeeeUVtftW9/tBzU407xQSERGRVtu5cydycnKk\nTqNO0tPTsWLFCqxevRpyuRwAoKOjU2Uarp2dHQAgIyOjzrE1FQcABgwYgAMHDmDWrFnQ09Orsd33\n33+Ptm3bqhyrnH74/F2vrKwsWFpaqmxy3rVrVwDArVu3VN6/atUqXLp0CWFhYWrlDDTu+AJ1Hxcp\n4tX1czZkfKnlYlFIREREajl9+jRsbGwgk8mwefNmAMDWrVthaGgIAwMDxMfHY/z48TAxMYG1tTX2\n7dunfG94eDjkcjnMzc2xYMECWFpaQi6Xw9nZGcnJycp2vr6+0NXVhYWFhfLYBx98AENDQ8hkMuTm\n5gIAFi5cCH9/f2RkZEAmk8He3h4AcOTIEZiYmGDt2rVNMSR1Fh4eDiEEJk+eXGu74uJiAICJiUmD\n+tNUHHXcvn0b+vr6sLW1VR6zs7OrUrhXPk9YWbhUMjU1xYgRIxAWFqb2NOWmHt/mrrrP2ZDxpZaL\nRSERERGpZdiwYThz5ozKMR8fH3z00UcoLi6GsbExoqKikJGRATs7O8yfPx+lpaUAnhV7c+bMQVFR\nEfz8/HDz5k1cvHgRZWVleOutt5CVlQXg2S/37u7uKn1s2bIFq1evVjkWFhaGSZMmoUePHhBCID09\nHQCUi2lUVFQ0yhjU1w8//IDevXvDwMCg1nY///wzgGdj3RCailNXRUVFOH78OObPnw9dXV3l8cDA\nQNy7dw+bNm2CQqFAamoqwsLC8Pbbb2Po0KFV4gwaNAi3b99GSkqKWv039fg2dzV9zvqOL7VcLAqJ\niIhIo5ydnWFiYoJOnTrB09MTjx8/RmZmpkobHR0d9OnTB3p6enBwcMDWrVuhUCiwe/dujeQwceJE\nFBQUYMWKFRqJpwmPHz/GjRs30KNHjxrb3L9/H5GRkfDz84OTk9NL73g1dhx1BQcHw9LSEmvWrFE5\nPmLECCxZsgS+vr4wMTFB3759oVAosGPHjmrj9OzZEwBw5cqVOvfdlOPb3L3sc9ZnfKll05E6ASIi\nImq5Ku8WVd4prImjoyMMDAxw7dq1pkhLEjk5ORBC1HoXy8nJCY8fP4a7uzvWrFmDdu3a1asvTcVR\nR2xsLKKjo/Hjjz/C2NhY5dyyZcuwY8cO/PTTTxgyZAhycnKwdOlSODk54cyZM8rnCytVjtH9+/fr\n3H9Tjm9z97LPWZ/xpZaNRSERERE1C3p6enjw4IHUaTSaJ0+eAECtC4qYm5tj586d+MMf/tCgvjQV\np64iIyMREhKCxMREWFlZqZy7e/cu1q1bh4CAAIwePRoAYGtri4iICJiammLDhg0IDw9XeY++vj6A\n/45ZXTTl+DZ3L/uc9RlfatlYFBIREZHkSktLkZ+fD2tra6lTaTSVv4jXtjl7p06d0KFDhwb3pak4\ndbFp0yYcPXoUx48fh5GRUZXzaWlpKC8vr1IsmpiYwMzMDKmpqVXe8/TpUwD/HbO6aMrxbe5e9jnr\nM77UsrEoJCIiIsklJiZCCKGy6IiOjs5Lp51qE3Nzc8hkMjx69KjGNi9uKVBfmopTGyEEli5diocP\nHyIuLg46OtX/WllZ6N+9e1fluEKhQF5eXpWpowCUY9S5c+c659OU49vcvexz1md8qWXjQjNERETU\n5CoqKvDw4UOUlZXh8uXLWLhwIWxsbDBnzhxlG3t7e+Tl5SEuLg6lpaV48OBBlT3tAMDMzAx37tzB\nzZs3oVAoUFpaioSEhGa3JYWBgQHs7OyQnZ1d7fn09HR07twZHh4eVc55enqic+fOuHjx4kv70VSc\nl7l69SrWr1+PiIgItGvXDjKZTOW1ceNGAM+mio4aNQoRERFISkpCcXExsrKy4OXlBQCYN29eldiV\nY9SvX786591U41sXUsar7XNWenF8iVgUEhERkVo2b96MwYMHAwCWLFmCKVOmYOvWrQgNDQUA9O/f\nH7/99hsiIiLg7+8PAHjnnXeQlpamjPHkyRP069cP+vr6cHFxQa9evXDixAmV58F8fHwwatQozJw5\nE71798ann36qnO7m5OSk3L7C29sb5ubmcHBwwIQJE5CXl9ck41AfEydORGpqqnL/uOfVtmfc06dP\nkZOTg/j4+Jf2oYk4586dw7Bhw2BlZYXk5GSkpKTA0tISb775JpKSkl7az/NkMhliYmLg6emJefPm\nwdTUFA4ODsjMzMSBAwfg4uJS5T3nz59Hly5d0L9/f7Xybuzxrcu4SBnvZZ+z0ovjSwRBRKRFZsyY\nIWbMmCF1GkRaKyoqSkj9v38vLy9hZmYmaQ7qAiCioqLq3N7Ly0t06dKlyvG0tDSho6Mjvv32W7X6\nLy8vFy4uLmLnzp1qva+x4jSm3NxcIZfLxcaNG5XH6pq31OOrDfGqG99Kfn5+4pVXXlE7prrfD2p2\nonmnkIiIiJpcbYuBtBTFxcU4evQo0tLSlAt72NvbIygoCEFBQSgsLKxTnPLycsTFxUGhUMDT07Pe\n+WgqTmNbtWoVBg4cCF9fXwDq5S3l+GpLvBfHVwiBO3fu4PTp00hPT29wfNJOLAqJiIiIGkFeXh7e\neecd9OrVC++9957yeEBAANzc3ODp6VnroiiVEhMTceDAASQkJNS6B19TxWlMISEhuHTpEg4fPqzc\nW0/dvKUaX22IV934xsfHo0uXLnBxccEPP/zQ4HxJS0l9r5KISB0tcfrovHnzhJGRkQAg/vWvfzVJ\nnxs2bBCdOnUSAMSXX37ZJH02hvLychESEiKcnJzqHWP//v3C1tZWABAAxPLly2tt/8UXXwgAQiaT\nid69e4uTJ0/Wu28h6nf9G3L9pJ4+GhAQIHR1dQUA0b17dxETEyNZLupAI0yPO3r0qFiyZIlGY2qz\nuLg4ERwcLMrKyjQSj+OrStPj+7zG+H5Qk4qWCVHHp4SJiJoBNzc3AEBMTIzEmWhWZGQkZs6ciX/9\n618YOHBgk/SZnp6Onj174ssvv8SCBQuapE9NSktLw9y5c/HPf/4TAwYMwKVLlxoUz97eHhkZGbCw\nsEBmZqbyr+jPKy8vR48ePXDr1i2MGTMGx44da1Cflepz/et7/aKjo+Hh4VHnRULoGZlMhqioKLi7\nu0udClGzw++H1ovh9FEiItI6KSkpWLp0Kby9vTVaRL/++uu4d+8e4uLiqj1/4MABdOnSRWP9ERER\nNQcsComImgGZTCZ1ClplwIABOHDgAGbNmqWyhUFD+fj4AAC+/PLLas+HhIQot1jQJF5/IiKSEotC\nImrxysvL8cknn8DGxgb6+vro378/oqKiAABbt26FoaEhDAwMEB8fj/Hjx8PExATW1tbYt29flVjf\nfvstHB0dIZfLYWhoiO7du+PTTz8F8GwFt5CQEPTp0wd6enowNTXF1KlTce3aNZUYQghs2LABvXv3\nhp6eHtq3b4+PP/5YrbzXr18PAwMDGBsbIycnB/7+/ujSpQuuX7/eoLE6deoUHBwc0L59e8jlcvTr\n1w9Hjx4FALz//vvKTal79OiBf/3rXwCAuXPnwsDAAO3bt8fBgwcly706R44cUWsD89GjR6NPnz44\nceJElXz++c9/oqioCOPGjav2vU15/YmIiDSJRSERtXhLly7F+vXrERoairt372LSpEl49913ceHC\nBfj4+OCjjz5CcXExjI2NERUVhYyMDNjZ2WH+/PkoLS1VxgkLC8Of//xnzJgxA3fu3EF2djYCAwOV\nxcOqVasQEBCAZcuWIScnB0lJScjKyoKLiwvu37+vjLNixQosWbIEXl5euH//Pu7du4elS5eqlffi\nxYuxaNEiFBYWIjg4GLa2thg6dGiDnxO7f/8+PDw8cPPmTdy5cwdGRkaYNWsWAGDHjh2YPn062rZt\ni1OnTmHQoEEAgN27d8PV1RV79uzB5MmTJcu9OpXbHlRUVNT5PZXP523btk3l+BdffIFFixbV+L6m\nvP5EREQaJdkaN0RE9aDu6qPFxcXCwMBAeHp6Ko8VFRUJPT094ePjI4QQYtmyZQKAKC4uVrbZsmWL\nACDS09OFEEI8ffpUdOjQQYwaNUolfllZmQgLCxNFRUXCyMhIpR8hhPj5558FABEUFKTs28DAQLz1\n1lsq7fbt26ey+mR981ZHWlraS1evDA4OFgBETk6OEEKIY8eOCQBizZo1yjaPHj0SPXv2VK5o1xS5\nP2/IkCFiwIABDY7To0cPcePGDZGfny8MDQ2FqampKCoqEkIIkZGRIaytrUVJSYlQKBQCgBgzZozy\nvVJc/7pcv+pIvfqotgJXVySqEb8fWo+b1xNRy3b9+nUUFRWhb9++ymP6+vqwsLCoMq3vebq6ugCg\nvFN4+fJl5Ofn4+2331Zp17ZtW/j5+SE1NRWFhYVwdHRUOT948GDo6uoiOTkZwLMVI4uKijBmzJhG\nyVvTKlfgrLzjNnr0aPTq1Qu7du1S3tmLjIyEp6cn2rZt26xyr6/27dvj3XffxcOHDxEZGQkACA0N\nhY+Pj/Ln4kXaeP0rpwLzVbcXAHh4eEieB198NccXaT8dqRMgImpMjx8/BgAsX74cy5cvVzlnaWlZ\n5zgFBQUAgA4dOlR7Pj8/HwBgZGRU5VyHDh2gUCgAANnZ2QCATp06NUne6vrhhx+wYcMGpKamoqCg\nQGX6LPCskFiwYAEWLVqEn376CWPHjsU333yDv//975Lnrkk+Pj6IiIjAtm3b4OrqipiYGPznP/+p\nsb02Xn8+n6geDw8PLFy4EE5OTlKnQtTseHh4SJ0CNRCLQiJq0Sp/+Q4NDcXChQvrHcfKygoAkJub\nW+35ymKx8pf/5+Xn58Pa2hoAIJfLAQAlJSW19qepvNWRmZkJV1dXTJs2Dbt27YKVlRU2bdqExYsX\nq7SbM2cOAgMDsWPHDnTt2hUmJibo1q2bpLlr2sCBAzF06FCcO3cOXl5ecHNzg6mpaY3ttfH6cz8x\n9Xh4eMDJyYnjRlQNFoXaj9NHiahF69q1K+RyeYM3Nu/evTvMzMzw448/Vnu+b9++MDIyqrIISHJy\nMp4+fYrXX39d2a5NmzY4efJkk+StjitXrqC0tBQ+Pj6ws7ODXC6vdlqQqakpPDw8EBcXh40bN2L+\n/Pkq56XIvTFUbk+xf/9+fPTRR7W2bQnXn4iIWi8WhUTUosnlcsydOxf79u3D1q1bUVBQgPLycmRn\nZ+Pu3bt1jqOnp4fAwEAkJSXB19cXt2/fRkVFBRQKBa5evQq5XA5/f3/ExsZiz549KCgowJUrV+Dt\n7Q1LS0t4eXkBeHYHaPr06di/fz927tyJgoICXL58Gdu3b2+UvNVhY2MDADh27BiePHmCtLQ05bNw\nL/L29kZJSQm+//57TJo0SfLca5KQkKDWlhTPc3d3R8eOHeHq6go7O7ta27aE609ERK2Y1EvdEBGp\nQ93VR4UQoqSkRCxZskTY2NgIHR0d0alTJzF9+nSRmpoqtmzZIgwMDAQA0bNnT5GRkSG2b98uTExM\nBADRrVs38euvvypjbd68WfTr10/I5XIhl8vFoEGDxJYtW4QQQlRUVIgNGzaInj17inbt2glTU1Ph\n6uoqrl+/rpKPQqEQ77//vnjllVeEkZGRGDZsmPjkk08EAGFtbS1SUlJemve6deuEvr6+ACC6du0q\nvv32W7XG5IsvvhCdO3cWAIShoaGYNm2aEEKIJUuWCDMzM9GhQwfh5uYmNm/eLACIHj16iMzMTJUY\ngwYNEgEBAWqPeUNzF0KIs2fPijfffFNYWloKAAKAsLCwEM7OzuLkyZPKdocPHxbGxsYqq6W+KDY2\nVvTo0UMAEB07dhQffvih8tzixYvFmTNnlP+9fPlyYWFhIQCINm3aCAcHB3Hq1CkhRNNe/5quX11w\n9dH6AVdXJKoRvx9aL1omRCNsDEVE1Ejc3NwAADExMRJnQhMnTsTmzZtha2srdSqkhujoaHh4eDTK\nvpAtmUwmQ1RUFJ8pJKoGvx9aL4bTR4mIqE6eX4n08uXLkMvlLAiJiIhaABaFREQtxLVr1+q0n5Sn\np2e94i9ZsgRpaWn49ddfMXfuXHz66adakzsRNdyxY8cQEBCAiooKuLq6wsbGBnK5HF26dMGUKVNw\n+fJlteJpKs6LMUNDQ+Hs7Fzt+aCgIDg4OMDExAR6enqwt7fH4sWLUVhYWKXt3r17MXjwYBgbG6Nb\nt26YO3cu7t27pzx/8OBBrFu3TrmPK5E2Y1FIRNRCvPrqqxBCvPRVuSG7ugwMDPDqq69i7NixWLVq\nFRwcHLQmdyJqmJUrVyI8PByBgYGoqKjAqVOnsHfvXuTl5eH06dMoLi7G8OHDcefOnTrH1FScSmlp\naRg+fDgWLVqEoqKiatscP34cH374IW7evInc3FwEBwcjLCxM+WhCpaioKMyaNQtubm7Izs5GfHw8\nkpKSMH78eJSVlQEAJk+eDLlcjjFjxij3KiXSViwKiYioTtasWYPy8nJkZmZWWXGUSB3FxcU13snR\npj5ai88//xyRkZGIjo6GsbExAMDJyQnDhg2DgYEBbG1tsXbtWjx69AhfffWVWrE1FSclJQVLly6F\nt7c3Bg4cWGM7IyMjeHl5wczMDMbGxnB3d4erqyuOHDmCrKwsZbu//e1vsLKywscff4z27dtj4MCB\nWLRoES5duqSyKrOfnx8GDBiACRMmKItFIm3EopCIiIia1M6dO5GTk6P1fbQG6enpWLFiBVavXg25\nXA4A0NHRwaFDh1TaVW7bkpGRUefYmooDAAMGDMCBAwcwa9Ys6Onp1dju+++/R9u2bVWOdezYEQBU\n7i5mZWXB0tJSZa/Wrl27AgBu3bql8v5Vq1bh0qVLCAsLUytnouaERSERERHVSgiBkJAQ9OnTB3p6\nejA1NcXUqVNx7do1ZRtfX1/o6urCwsJCeeyDDz6AoaEhZDIZcnNzAQALFy6Ev78/MjIyIJPJYG9v\nj/DwcMjlcpibm2PBggWwtLSEXC6Hs7Ozyl2ZhvQBAEeOHKn3vpWtVXh4OIQQmDx5cq3tiouLAQAm\nJiYN6k9TcdRx+/Zt6OvrqyycZWdnV+WPCpXPE764b6mpqSlGjBiBsLAwrupLWotFIREREdVq1apV\nCAgIwLJly5CTk4OkpCRkZWXBxcUF9+/fB/CseHhxOfotW7Zg9erVKsfCwsIwadIk9OjRA0IIpKen\nw9fXF3PmzEFRURH8/Pxw8+ZNXLx4EWVlZXjrrbeU0/oa0gcA5YIgFRUVmhucFu6HH35A7969YWBg\nUGu7n3/+GQAwbNiwBvWnqTh1VVRUhOPHj2P+/PnQ1dVVHg8MDMS9e/ewadMmKBQKpKamIiwsDG+/\n/TaGDh1aJc6gQYNw+/ZtpKSkNEneRJrGopCIiIhqVFxcjJCQEEybNg2zZ89G+/bt0a9fP2zbtg25\nubnYvn27xvrS0dFR3o10cHDA1q1boVAosHv3bo3EnzhxIgoKCrBixQqNxGvpHj9+jBs3bqBHjx41\ntrl//z4iIyPh5+cHJyenl95RbOw46goODoalpSXWrFmjcnzEiBFYsmQJfH19YWJigr59+0KhUGDH\njh3VxunZsycA4MqVK42eM1FjYFFIRERENUpNTUVhYSEcHR1Vjg8ePBi6uroq0zs1zdHREQYGBirT\nVKnp5OTkQAhR611CJycn+Pn5YerUqUhISEC7du3q1Zem4qgjNjYW0dHROHr0qHIBnUrLli3D9u3b\n8dNPP6GwsBC//fYbnJ2d4eTkpLIgTaXKMaq8c06kbVgUEhERUY0ql9o3MjKqcq5Dhw5QKBSN2r+e\nnh4ePHjQqH1Q9Z48eQIAtS7cYm5ujuPHj2PTpk1o3759vfvSVJy6ioyMxOeff47ExER0795d5dzd\nu3exbt06/PWvf8Xo0aNhaGgIW1tbRERE4M6dO9iwYUOVePr6+gD+O2ZE2kZH6gSIiIio+erQoQMA\nVFv85efnw9rautH6Li0tbfQ+qGaVhU5tm7N36tRJ+TPSEJqKUxebNm3C0aNHcfz48Wr/2JGWloby\n8nJYWVmpHDcxMYGZmRlSU1OrvOfp06cA/jtmRNqGRSERERHVqG/fvjAyMsKFCxdUjicnJ+Pp06d4\n/fXXlcd0dHRQWlqqsb4TExMhhFBZ2EPTfVDNzM3NIZPJ8OjRoxrbvLilRH1pKk5thBBYunQpHj58\niLi4OOjoVP9rcOUfIe7evatyXKFQIC8vT7k1xfMqx6hz584azpqoaXD6KBEREdVILpfD398fsbGx\n2LNnDwoKCnDlyhV4e3vD0tISXl5eyrb29vbIy8tDXFwcSktL8eDBgyp7ugGAmZkZ7ty5g5s3b0Kh\nUCiLvIqKCjx8+BBlZWW4fPkyFi5cCBsbG8yZM0cjfSQkJHBLCjUYGBjAzs4O2dnZ1Z5PT09H586d\n4eHhUeWcp6cnOnfujIsXL760H03FeZmrV69i/fr1iIiIQLt27SCTyVReGzduBADY2tpi1KhRiIiI\nQFJSEoqLi5GVlaX8WZ83KgvQeQAAAvRJREFUb16V2JVj1K9fvwbnSSQFFoVERERUq5UrVyI4OBhB\nQUHo2LEjRowYge7duyMxMRGGhobKdj4+Phg1ahRmzpyJ3r1749NPP1VOp3t+gQ5vb2+Ym5vDwcEB\nEyZMQF5eHoBnz2P169cP+vr6cHFxQa9evXDixAmVZ9oa2gepZ+LEiUhNTVXuH/i82vbke/r0KXJy\nchAfH//SPjQR59y5cxg2bBisrKyQnJyMlJQUWFpa4s0330RSUtJL+3meTCZDTEwMPD09MW/ePJia\nmsLBwQGZmZk4cOAAXFxcqrzn/Pnz6NKlC/r371+nPoiaG5ngLptEpEXc3NwAADExMRJnQqSdoqOj\n4eHh0ew22V6wYAFiYmLw+++/S51KtWQyGaKioqrsk9jSpaeno0+fPti9ezdmz55d5/dVVFRg5MiR\nmDNnDt57771696+pOI3p999/h7W1NdasWQN/f3+p05FEa/1+tCAxvFNIREREzUJtC5qQNOzt7REU\nFISgoCAUFhbW6T3l5eWIi4uDQqGAp6dnvfvWVJzGtmrVKgwcOBC+vr5Sp0JUbywKiYiIiKhGAQEB\ncHNzg6enZ62LzlRKTEzEgQMHkJCQUOseh00VpzGFhITg0qVLOHz4cJPsrUjUWFgUEhERkaQCAwOx\ne/duPHr0CLa2tti/f7/UKdEL1q5dC19fX3z22WcvbTtmzBj8/e9/h4WFRYP61FScxhIfH4+SkhIk\nJibC1NRU6nSIGoRbUhAREZGkgoODERwcLHUa9BLjxo3DuHHjpE6j2ZgyZQqmTJkidRpEGsE7hURE\nRERERK0Yi0IiIiIiIqJWjEUhERERERFRK8aikIiIiIiIqBXjQjNEpHWys7MRHR0tdRpEWuns2bMA\nwO9QPVSOHRFRSyMTQgipkyAiqis3NzcuV09ERNTMREVFwd3dXeo0qH5iWBQSERERERG1XjF8ppCI\niIiIiKgVY1FIRERERETUirEoJCIiIiIiasVYFBIREREREbVi/wcudO3EWQ4JDQAAAABJRU5ErkJg\ngg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "af66azvgW9P-"
      },
      "source": [
        "### Decoder Layer\n",
        "\n",
        "Each decoder layer consists of sublayers:\n",
        "\n",
        "1.   Masked multi-head attention (with look ahead mask and padding mask)\n",
        "2.   Multi-head attention (with padding mask). `value` and `key` receive the *encoder output* as inputs. `query` receives the *output from the masked multi-head attention sublayer.*\n",
        "3.   2 dense layers followed by dropout\n",
        "\n",
        "Each of these sublayers has a residual connection around it followed by a layer normalization. The output of each sublayer is `LayerNorm(x + Sublayer(x))`. The normalization is done on the `d_model` (last) axis.\n",
        "\n",
        "As `query` receives the output from decoder's first attention block, and `key` receives the encoder output, the attention weights represent the importance given to the decoder's input based on the encoder's output. In other words, the decoder predicts the next word by looking at the encoder output and self-attending to its own output. See the demonstration above in the scaled dot product attention section."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6mLvvNMWgDnf",
        "colab": {}
      },
      "source": [
        "def decoder_layer(units, d_model, num_heads, dropout, name=\"decoder_layer\"):\n",
        "  inputs = tf.keras.Input(shape=(None, d_model), name=\"inputs\")\n",
        "  enc_outputs = tf.keras.Input(shape=(None, d_model), name=\"encoder_outputs\")\n",
        "  look_ahead_mask = tf.keras.Input(\n",
        "      shape=(1, None, None), name=\"look_ahead_mask\")\n",
        "  padding_mask = tf.keras.Input(shape=(1, 1, None), name='padding_mask')\n",
        "\n",
        "  attention1 = MultiHeadAttention(\n",
        "      d_model, num_heads, name=\"attention_1\")(inputs={\n",
        "          'query': inputs,\n",
        "          'key': inputs,\n",
        "          'value': inputs,\n",
        "          'mask': look_ahead_mask\n",
        "      })\n",
        "  attention1 = tf.keras.layers.LayerNormalization(\n",
        "      epsilon=1e-6)(attention1 + inputs)\n",
        "\n",
        "  attention2 = MultiHeadAttention(\n",
        "      d_model, num_heads, name=\"attention_2\")(inputs={\n",
        "          'query': attention1,\n",
        "          'key': enc_outputs,\n",
        "          'value': enc_outputs,\n",
        "          'mask': padding_mask\n",
        "      })\n",
        "  attention2 = tf.keras.layers.Dropout(rate=dropout)(attention2)\n",
        "  attention2 = tf.keras.layers.LayerNormalization(\n",
        "      epsilon=1e-6)(attention2 + attention1)\n",
        "\n",
        "  outputs = tf.keras.layers.Dense(units=units, activation='relu')(attention2)\n",
        "  outputs = tf.keras.layers.Dense(units=d_model)(outputs)\n",
        "  outputs = tf.keras.layers.Dropout(rate=dropout)(outputs)\n",
        "  outputs = tf.keras.layers.LayerNormalization(\n",
        "      epsilon=1e-6)(outputs + attention2)\n",
        "\n",
        "  return tf.keras.Model(\n",
        "      inputs=[inputs, enc_outputs, look_ahead_mask, padding_mask],\n",
        "      outputs=outputs,\n",
        "      name=name)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8M1NrQ_NgEaM",
        "outputId": "30dfd728-8c6d-4021-e5ac-54675cb4d55c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "sample_decoder_layer = decoder_layer(\n",
        "    units=512,\n",
        "    d_model=128,\n",
        "    num_heads=4,\n",
        "    dropout=0.3,\n",
        "    name=\"sample_decoder_layer\")\n",
        "\n",
        "tf.keras.utils.plot_model(\n",
        "    sample_decoder_layer, to_file='decoder_layer.png', show_shapes=True)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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Oo1bo06cP+vTpI3UYkhs0aBAGDRokdRgE/R/kWlcx95gu5p7/Yu7RJUUO4jcfiIiIiIiI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\naKEKFSqoffv2atKkifb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w7sXnb9myxWRlZZmM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W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\nT8vnZwxJ2rRpk6xWq0JCQgp0viRp3rx5MsaoR48e2fZLTk6WJFmt1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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "NPSKnjS-gE_q"
      },
      "source": [
        "### Decoder\n",
        "\n",
        "The Decoder consists of:\n",
        "1.   Output Embedding\n",
        "2.   Positional Encoding\n",
        "3.   N decoder layers\n",
        "\n",
        "The target is put through an embedding which is summed with the positional encoding. The output of this summation is the input to the decoder layers. The output of the decoder is the input to the final linear layer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "dYRx7YzCW4bu",
        "colab": {}
      },
      "source": [
        "def decoder(vocab_size,\n",
        "            num_layers,\n",
        "            units,\n",
        "            d_model,\n",
        "            num_heads,\n",
        "            dropout,\n",
        "            name='decoder'):\n",
        "  inputs = tf.keras.Input(shape=(None,), name='inputs')\n",
        "  enc_outputs = tf.keras.Input(shape=(None, d_model), name='encoder_outputs')\n",
        "  look_ahead_mask = tf.keras.Input(\n",
        "      shape=(1, None, None), name='look_ahead_mask')\n",
        "  padding_mask = tf.keras.Input(shape=(1, 1, None), name='padding_mask')\n",
        "  \n",
        "  embeddings = tf.keras.layers.Embedding(vocab_size, d_model)(inputs)\n",
        "  embeddings *= tf.math.sqrt(tf.cast(d_model, tf.float32))\n",
        "  embeddings = PositionalEncoding(vocab_size, d_model)(embeddings)\n",
        "\n",
        "  outputs = tf.keras.layers.Dropout(rate=dropout)(embeddings)\n",
        "\n",
        "  for i in range(num_layers):\n",
        "    outputs = decoder_layer(\n",
        "        units=units,\n",
        "        d_model=d_model,\n",
        "        num_heads=num_heads,\n",
        "        dropout=dropout,\n",
        "        name='decoder_layer_{}'.format(i),\n",
        "    )(inputs=[outputs, enc_outputs, look_ahead_mask, padding_mask])\n",
        "\n",
        "  return tf.keras.Model(\n",
        "      inputs=[inputs, enc_outputs, look_ahead_mask, padding_mask],\n",
        "      outputs=outputs,\n",
        "      name=name)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "tUdK8jb9hlTZ",
        "outputId": "3e9d5a6c-b223-483b-d50f-be37a11d11bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 774
        }
      },
      "source": [
        "sample_decoder = decoder(\n",
        "    vocab_size=8192,\n",
        "    num_layers=2,\n",
        "    units=512,\n",
        "    d_model=128,\n",
        "    num_heads=4,\n",
        "    dropout=0.3,\n",
        "    name=\"sample_decoder\")\n",
        "\n",
        "tf.keras.utils.plot_model(\n",
        "    sample_decoder, to_file='decoder.png', show_shapes=True)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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MAdO3Y4OzsTFYZaWb16Nb8fsHTpUsFdqampUVFRXC73rbfesrW1pVAo1tbWCxYsyMvL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\ngAE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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "yl0o97RJXAqw"
      },
      "source": [
        "### Transformer\n",
        "\n",
        "Transformer consists of the encoder, decoder and a final linear layer. The output of the decoder is the input to the linear layer and its output is returned."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "TW-v7Fz6XAfC",
        "colab": {}
      },
      "source": [
        "def transformer(vocab_size,\n",
        "                num_layers,\n",
        "                units,\n",
        "                d_model,\n",
        "                num_heads,\n",
        "                dropout,\n",
        "                name=\"transformer\"):\n",
        "  inputs = tf.keras.Input(shape=(None,), name=\"inputs\")\n",
        "  dec_inputs = tf.keras.Input(shape=(None,), name=\"dec_inputs\")\n",
        "\n",
        "  enc_padding_mask = tf.keras.layers.Lambda(\n",
        "      create_padding_mask, output_shape=(1, 1, None),\n",
        "      name='enc_padding_mask')(inputs)\n",
        "  # mask the future tokens for decoder inputs at the 1st attention block\n",
        "  look_ahead_mask = tf.keras.layers.Lambda(\n",
        "      create_look_ahead_mask,\n",
        "      output_shape=(1, None, None),\n",
        "      name='look_ahead_mask')(dec_inputs)\n",
        "  # mask the encoder outputs for the 2nd attention block\n",
        "  dec_padding_mask = tf.keras.layers.Lambda(\n",
        "      create_padding_mask, output_shape=(1, 1, None),\n",
        "      name='dec_padding_mask')(inputs)\n",
        "\n",
        "  enc_outputs = encoder(\n",
        "      vocab_size=vocab_size,\n",
        "      num_layers=num_layers,\n",
        "      units=units,\n",
        "      d_model=d_model,\n",
        "      num_heads=num_heads,\n",
        "      dropout=dropout,\n",
        "  )(inputs=[inputs, enc_padding_mask])\n",
        "\n",
        "  dec_outputs = decoder(\n",
        "      vocab_size=vocab_size,\n",
        "      num_layers=num_layers,\n",
        "      units=units,\n",
        "      d_model=d_model,\n",
        "      num_heads=num_heads,\n",
        "      dropout=dropout,\n",
        "  )(inputs=[dec_inputs, enc_outputs, look_ahead_mask, dec_padding_mask])\n",
        "\n",
        "  outputs = tf.keras.layers.Dense(units=vocab_size, name=\"outputs\")(dec_outputs)\n",
        "\n",
        "  return tf.keras.Model(inputs=[inputs, dec_inputs], outputs=outputs, name=name)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "aihJLVq_iJ_T",
        "outputId": "cb623cf5-f99d-47f7-a1a6-468e7f290b71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        }
      },
      "source": [
        "sample_transformer = transformer(\n",
        "    vocab_size=8192,\n",
        "    num_layers=4,\n",
        "    units=512,\n",
        "    d_model=128,\n",
        "    num_heads=4,\n",
        "    dropout=0.3,\n",
        "    name=\"sample_transformer\")\n",
        "\n",
        "tf.keras.utils.plot_model(\n",
        "    sample_transformer, to_file='transformer.png', show_shapes=True)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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aevLkyWHDhjEdBxTQtWvXPDw8rKysfv/99wEDBjAdBwAAAACkDfcMA2DM9evX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\nyy9Tp05V7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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9HD7GK-nh_KT"
      },
      "source": [
        "## Train model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "PDDxNpA-5Q5t"
      },
      "source": [
        "### Initialize model\n",
        "\n",
        "To keep this example small and relatively fast, the values for *num_layers, d_model, and units* have been reduced. See the [paper](https://arxiv.org/abs/1706.03762) for all the other versions of the transformer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "xE3unrOT5M5z",
        "colab": {}
      },
      "source": [
        "tf.keras.backend.clear_session()\n",
        "\n",
        "# Hyper-parameters\n",
        "NUM_LAYERS = 2\n",
        "D_MODEL = 256\n",
        "NUM_HEADS = 8\n",
        "UNITS = 512\n",
        "DROPOUT = 0.1\n",
        "\n",
        "model = transformer(\n",
        "    vocab_size=VOCAB_SIZE,\n",
        "    num_layers=NUM_LAYERS,\n",
        "    units=UNITS,\n",
        "    d_model=D_MODEL,\n",
        "    num_heads=NUM_HEADS,\n",
        "    dropout=DROPOUT)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "0_GCb0LaV1tI"
      },
      "source": [
        "### Loss function\n",
        "\n",
        "Since the target sequences are padded, it is important to apply a padding mask when calculating the loss."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "UInVM9iGAMv1",
        "colab": {}
      },
      "source": [
        "def loss_function(y_true, y_pred):\n",
        "  y_true = tf.reshape(y_true, shape=(-1, MAX_LENGTH - 1))\n",
        "  \n",
        "  loss = tf.keras.losses.SparseCategoricalCrossentropy(\n",
        "      from_logits=True, reduction='none')(y_true, y_pred)\n",
        "\n",
        "  mask = tf.cast(tf.not_equal(y_true, 0), tf.float32)\n",
        "  loss = tf.multiply(loss, mask)\n",
        "\n",
        "  return tf.reduce_mean(loss)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "XvFM9ajSVybP"
      },
      "source": [
        "### Custom learning rate\n",
        "\n",
        "Use the Adam optimizer with a custom learning rate scheduler according to the formula in the [paper](https://arxiv.org/abs/1706.03762).\n",
        "\n",
        "$$\\Large{lrate = d_{model}^{-0.5} * min(step{\\_}num^{-0.5}, step{\\_}num * warmup{\\_}steps^{-1.5})}$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "WW3SeLDhAMJd",
        "colab": {}
      },
      "source": [
        "class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):\n",
        "\n",
        "  def __init__(self, d_model, warmup_steps=4000):\n",
        "    super(CustomSchedule, self).__init__()\n",
        "\n",
        "    self.d_model = d_model\n",
        "    self.d_model = tf.cast(self.d_model, tf.float32)\n",
        "\n",
        "    self.warmup_steps = warmup_steps\n",
        "\n",
        "  def __call__(self, step):\n",
        "    arg1 = tf.math.rsqrt(step)\n",
        "    arg2 = step * (self.warmup_steps**-1.5)\n",
        "\n",
        "    return tf.math.rsqrt(self.d_model) * tf.math.minimum(arg1, arg2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "67BoG_UeaHHw",
        "outputId": "904f2155-eed1-4533-d6f4-83f06d26a5cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "sample_learning_rate = CustomSchedule(d_model=128)\n",
        "\n",
        "plt.plot(sample_learning_rate(tf.range(200000, dtype=tf.float32)))\n",
        "plt.ylabel(\"Learning Rate\")\n",
        "plt.xlabel(\"Train Step\")"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Train Step')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hjTfcz4e+v4x/f3wjhzr1pAeRYjEmrmcxsynApcDbBsrr7jcDNwO0t7cPqQ+n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\nCjAiIhILBRgREYmFAoyIiMTi/wMZIzDctONGFgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "cCqve3kwWCxd"
      },
      "source": [
        "### Compile Model\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1QqojIa5WEQq",
        "colab": {}
      },
      "source": [
        "learning_rate = CustomSchedule(D_MODEL)\n",
        "\n",
        "optimizer = tf.keras.optimizers.Adam(\n",
        "    learning_rate, beta_1=0.9, beta_2=0.98, epsilon=1e-9)\n",
        "\n",
        "def accuracy(y_true, y_pred):\n",
        "  # ensure labels have shape (batch_size, MAX_LENGTH - 1)\n",
        "  y_true = tf.reshape(y_true, shape=(-1, MAX_LENGTH - 1))\n",
        "  return tf.keras.metrics.sparse_categorical_accuracy(y_true, y_pred)\n",
        "\n",
        "model.compile(optimizer=optimizer, loss=loss_function, metrics=[accuracy])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "vDMd69urLNuc"
      },
      "source": [
        "### Fit model\n",
        "\n",
        "Train our transformer by simply calling `model.fit()`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "d7iahRzlLNG2",
        "outputId": "244d0fcb-eb0f-415f-a8a0-46145fab9998",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        }
      },
      "source": [
        "EPOCHS = 20\n",
        "\n",
        "model.fit(dataset, epochs=EPOCHS)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "689/689 [==============================] - 114s 165ms/step - loss: 2.1129 - accuracy: 0.0421\n",
            "Epoch 2/20\n",
            "689/689 [==============================] - 104s 150ms/step - loss: 1.5029 - accuracy: 0.0784\n",
            "Epoch 3/20\n",
            "689/689 [==============================] - 104s 150ms/step - loss: 1.3981 - accuracy: 0.0855\n",
            "Epoch 4/20\n",
            "689/689 [==============================] - 103s 149ms/step - loss: 1.3386 - accuracy: 0.0902\n",
            "Epoch 5/20\n",
            "689/689 [==============================] - 103s 150ms/step - loss: 1.2868 - accuracy: 0.0941\n",
            "Epoch 6/20\n",
            "689/689 [==============================] - 103s 150ms/step - loss: 1.2402 - accuracy: 0.0978\n",
            "Epoch 7/20\n",
            "689/689 [==============================] - 104s 151ms/step - loss: 1.1851 - accuracy: 0.1020\n",
            "Epoch 8/20\n",
            "689/689 [==============================] - 104s 151ms/step - loss: 1.1236 - accuracy: 0.1075\n",
            "Epoch 9/20\n",
            "689/689 [==============================] - 104s 151ms/step - loss: 1.0677 - accuracy: 0.1129\n",
            "Epoch 10/20\n",
            "689/689 [==============================] - 104s 150ms/step - loss: 1.0145 - accuracy: 0.1187\n",
            "Epoch 11/20\n",
            "689/689 [==============================] - 103s 150ms/step - loss: 0.9655 - accuracy: 0.1245\n",
            "Epoch 12/20\n",
            "689/689 [==============================] - 102s 148ms/step - loss: 0.9215 - accuracy: 0.1302\n",
            "Epoch 13/20\n",
            "689/689 [==============================] - 104s 151ms/step - loss: 0.8806 - accuracy: 0.1356\n",
            "Epoch 14/20\n",
            "689/689 [==============================] - 104s 152ms/step - loss: 0.8434 - accuracy: 0.1411\n",
            "Epoch 15/20\n",
            "689/689 [==============================] - 104s 151ms/step - loss: 0.8093 - accuracy: 0.1463\n",
            "Epoch 16/20\n",
            "689/689 [==============================] - 105s 153ms/step - loss: 0.7789 - accuracy: 0.1510\n",
            "Epoch 17/20\n",
            "689/689 [==============================] - 105s 152ms/step - loss: 0.7505 - accuracy: 0.1554\n",
            "Epoch 18/20\n",
            "689/689 [==============================] - 105s 153ms/step - loss: 0.7242 - accuracy: 0.1601\n",
            "Epoch 19/20\n",
            "689/689 [==============================] - 105s 152ms/step - loss: 0.7005 - accuracy: 0.1638\n",
            "Epoch 20/20\n",
            "689/689 [==============================] - 104s 151ms/step - loss: 0.6788 - accuracy: 0.1676\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f94d3a1c3c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "p1DUXog6WqV-"
      },
      "source": [
        "## Evaluate and predict\n",
        "\n",
        "The following steps are used for evaluation:\n",
        "\n",
        "* Apply the same preprocessing method we used to create our dataset for the input sentence.\n",
        "* Tokenize the input sentence and add `START_TOKEN` and `END_TOKEN`. \n",
        "* Calculate the padding masks and the look ahead masks.\n",
        "* The decoder then outputs the predictions by looking at the encoder output and its own output.\n",
        "* Select the last word and calculate the argmax of that.\n",
        "* Concatentate the predicted word to the decoder input as pass it to the decoder.\n",
        "* In this approach, the decoder predicts the next word based on the previous words it predicted.\n",
        "\n",
        "Note: The model used here has less capacity and trained on a subset of the full dataset, hence its performance can be further improved."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_NjsS3zuAbRn",
        "colab": {}
      },
      "source": [
        "def evaluate(sentence):\n",
        "  sentence = preprocess_sentence(sentence)\n",
        "\n",
        "  sentence = tf.expand_dims(\n",
        "      START_TOKEN + tokenizer.encode(sentence) + END_TOKEN, axis=0)\n",
        "\n",
        "  output = tf.expand_dims(START_TOKEN, 0)\n",
        "\n",
        "  for i in range(MAX_LENGTH):\n",
        "    predictions = model(inputs=[sentence, output], training=False)\n",
        "\n",
        "    # select the last word from the seq_len dimension\n",
        "    predictions = predictions[:, -1:, :]\n",
        "    predicted_id = tf.cast(tf.argmax(predictions, axis=-1), tf.int32)\n",
        "\n",
        "    # return the result if the predicted_id is equal to the end token\n",
        "    if tf.equal(predicted_id, END_TOKEN[0]):\n",
        "      break\n",
        "\n",
        "    # concatenated the predicted_id to the output which is given to the decoder\n",
        "    # as its input.\n",
        "    output = tf.concat([output, predicted_id], axis=-1)\n",
        "\n",
        "  return tf.squeeze(output, axis=0)\n",
        "\n",
        "\n",
        "def predict(sentence):\n",
        "  prediction = evaluate(sentence)\n",
        "\n",
        "  predicted_sentence = tokenizer.decode(\n",
        "      [i for i in prediction if i < tokenizer.vocab_size])\n",
        "\n",
        "  print('Input: {}'.format(sentence))\n",
        "  print('Output: {}'.format(predicted_sentence))\n",
        "\n",
        "  return predicted_sentence"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9J3Jdtk2P-RT"
      },
      "source": [
        "Let's test our model!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6IeMSGEgRTvC",
        "outputId": "5b98cd62-4a42-4b5c-8ddb-222cab99a639",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "output = predict('Where have you been?')"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input: Where have you been?\n",
            "Output: i don t know .\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ivVgU6ydRV8R",
        "outputId": "121ae5c7-7447-4d06-e2dd-563c8eae76ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "output = predict(\"It's a trap\")"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input: It's a trap\n",
            "Output: i don t think it s a damn good idea .\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "s5zG7i8KAtRU",
        "outputId": "9346903c-3958-45ab-d1bb-e56cc591bbbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        }
      },
      "source": [
        "# feed the model with its previous output\n",
        "sentence = 'I am not crazy, my mother had me tested.'\n",
        "for _ in range(5):\n",
        "  sentence = predict(sentence)\n",
        "  print('')"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input: I am not crazy, my mother had me tested.\n",
            "Output: well , i d like to ask you something , but i d like to have a baby .\n",
            "\n",
            "Input: well , i d like to ask you something , but i d like to have a baby .\n",
            "Output: i m sorry , i m sorry . i m very welcome .\n",
            "\n",
            "Input: i m sorry , i m sorry . i m very welcome .\n",
            "Output: oh , i m sorry .\n",
            "\n",
            "Input: oh , i m sorry .\n",
            "Output: i don t know . i just don t think it s because i love you .\n",
            "\n",
            "Input: i don t know . i just don t think it s because i love you .\n",
            "Output: i don t think so . i just want to be a marty , you were good idea .\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "5-dGPc14QnAP"
      },
      "source": [
        "## Summary\n",
        "\n",
        "Here we are, we have implemented a Transformer in TensorFlow 2.0 in around 500 lines of code.\n",
        "\n",
        "In this tutorial, we focus on the two different approaches to implement complex models with Functional API and Model subclassing, and how to incorporate them.\n",
        "\n",
        "Try using a different dataset or hyper-parameters to train the Transformer! Thanks for reading.\n"
      ]
    }
  ]
}